# An Early Investigation into the Utility of Multimodal Large Language Models
in Medical Imaging

Sulaiman Khan1, Md. Rafiul Biswas1, Alina Murad2, Hazrat Ali, Senior Member,
IEEE3, Zubair Shah1  3Faculty of Computing and Information Technology,
Sohar University, Sohar, Oman.
Email<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>2Foundation University School of
Sciences and Technology, Rawalpindi, Pakistan.  1College of Science and
Engineering, Hamad Bin Khalifa University, Qatar Foundation,
Doha, Qatar.

###### Abstract

Recent developments in multimodal large language models (MLLMs) have spurred
significant interest in their potential applications across various medical
imaging domains. On the one hand, there is a temptation to use these
generative models to synthesize realistic-looking medical image data, while on
the other hand, the ability to identify synthetic image data in a pool of data
is also significantly important. In this study, we explore the potential of
the Gemini (gemini-1.0-pro-vision-latest) and GPT-4V (gpt-4-vision-preview)
models for medical image analysis using two modalities of medical image data.
Utilizing synthetic and real imaging data, both Gemini AI and GPT-4V are first
used to classify real versus synthetic images, followed by an interpretation
and analysis of the input images. Experimental results demonstrate that both
Gemini and GPT-4 could perform some interpretation of the input images. In
this specific experiment, Gemini was able to perform slightly better than the
GPT-4V on the classification task. In contrast, responses associated with
GPT-4V were mostly generic in nature. Our early investigation presented in
this work provides insights into the potential of MLLMs to assist with the
classification and interpretation of retinal fundoscopy and lung X-ray images.
We also identify key limitations associated with the early investigation study
on MLLMs for specialized tasks in medical image analysis.

###### Index Terms:

LLM, ChatGPT, Gemini AI, Multimodal data, Retina, Lung

##  I Introduction

With the public introduction of large language models (LLMs)-enabled tools,
there has been an unprecedented surge of interest to explore and use these
tools. LLMs, designed for comprehending and generating human-like text, truly
revolutionized various domains, including content creation, document
summarization, and information retrieval. Among these, ChatGPT, powered by the
Generative Pre-trained Transformer (GPT) engine, emerged as the most prominent
tool, and attracted over a 100 million unique users within two months of its
release [1], [2].

LLMs were primarily text-based, lacking the ability to process other
modalities. While leveraging LLMs in textual contexts holds significant
implications for medical documentation, healthcare practices heavily rely on
visual (medical imaging) and multimodal data. In this context, the recent
advancements in Multimodal LLMs (MLLMs)-based tools such as Gemini and ChatGPT
(GPT-4V) may result in promising solutions in medical image analysis. While
both Gemini and ChatGPT tools have been widely explored for natural image
interpretation and image-to-text or text-to-image applications in computer
vision, their existing capabilities in medical image interpretation have not
been explored. This paper focuses on applications of MLLMs for medical image
analysis applications. More specifically, this work explores Gemini and GPT-4V
MLLMs for the classification and interpretation of medical images, especially
for lung X-ray images (pulmonary disease) and retinal fundoscopy images
(ophthalmology), which makes this work an early study on MLLMs for medical
image analysis. Following are the insights of this research work:

  * •

Image classification: We explore pre-trained MLLMs to handle the
identification of synthetic lung X-ray and retinal fundoscopy images.

  * •

Disease diagnosing: We explore pre-trained MLLMs to interpret medical images
and possibly identify disease-related symptoms in the input images. This could
prove to be a bridge between healthcare professionals and the utilization of
AI for medical purposes.

  * •

Prompt formulation: We adopt the NERIF (Notation-Enhanced Rubric Instruction
for Few-shot Learning) method, proposed by Lee and Zhai [3], to formulate an
effective input prompt that effectively align with our intended application of
the MLLM.

  * •

Comparative analysis: We present a comparative analysis of the performance
achieved by Gemini and GPT-4V and also include subjective input from a trained
practicing doctor (at hospital name anonymized for peer-review) to help the
reader get an estimate of the MLLM’s performance. Even though the findings are
non-binding, based on our limited experimentation, Gemini demonstrates better
image understanding performance than GPT-4V.

The rest of the paper is organized as follows: Section II briefly explains the
architectural and operational differences between Gemini and GPT-4V. We then
elaborate our methodology in Section III, which outlines the data sources used
in this work and the prompts formulated to perform classification and
interpretation using MLLMs. We present the results and the critical findings
in Section IV. We also identify the fundamental limitations of this work in
the discussion. Finally, we provide valuable recommendations and conclude the
paper in Section V.

##  II Gemini versus GPT-4V

The development of MLLMs represents a significant advancement in Artificial
Intelligence (AI), by incorporating multi-sensory capabilities to enhance
general intelligence, thereby facilitating more natural human-computer
interactions [4]. MLLMs operate within a shared embedding space, enabling the
integration and processing of diverse data modalities such as text, image,
audio, video, and 3D content. The latest MLLMs, such as GPT-4 with Vision
(also known as GPT-4V or gpt-4-vision-preview in the API) and Google
DeepMind’s Gemini pro-vision or simply Gemini AI, can accept inputs of images
and/or text to perform a wide range of language, vision, and vision-language
tasks. These tasks include language translation and coding [5], image
recognition [6], object localization [4], visual question answering [7], and
visual dialogue [8]. Moreover, within the realm of AI for healthcare, both
Gemini and GPT-4V find applications in image analysis, such as ophthalmology
[9], pulmonary disease diagnosing and interpretations [10], where data often
comes in visual formats like medical images/scans. A detailed comparison of
the architecture, training approach, training datasets, performance metrics,
capabilities, and applications of these state-of-the-art models can shed light
on their usability and advantages, particularly within healthcare domains.

GPT-4V is popular for its proficiency in handling intricate language and
image-processing tasks and for its adeptness in understanding complex contexts
[11]. On the other hand, Gemini, a tool developed by Google DeepMind,
represents a large multimodal model that extends beyond text and images to
encompass inputs from audio and video sources. This broader spectrum of input
types indicates a more adaptable and comprehensive approach to multimodal
learning from complex medical data [12]. Gemini undergoes training using
Google’s tensor processing unit (TPU) [13], renowned for its high
computational capabilities. With an extended 32K context length, Gemini can
process significantly larger data chunks simultaneously, potentially
facilitating deeper insights and understanding in complex tasks [12]. TPUs are
trained to accommodate a 32k context length and utilize efficient attention
mechanisms such as multi-query attention [14].

GPT-4V has been trained extensively on a large dataset that includes online
image data, as well as various textual sources like books, journals, code, and
other text data [11]. Through rigorous training, GPT-4V has attained the
capability to process and generate both language and images that closely
resemble human-created content. As a generative model, GPT-4V can produce new
content in response to both image and textual inputs. Leveraging its dual-
transformer design and built-in image decoding modules, it efficiently
analyzes and comprehends images by harnessing vast amounts of textual data.
This enables it to excel in tasks such as translating languages depicted in
images, facilitating cross-language communication through images, and
generating various forms of artistic content. Additionally, GPT-4V can provide
explainable responses to user queries.

On the other hand, Gemini model is trained on a dataset that is both
multimodal and multilingual. This dataset encompasses data from web documents,
books, and code, including image, audio, and video data. Utilizing the
SentencePiece tokenizer [15], Gemini refines its tokenizer by training it on a
large subset of the entire training corpus, resulting in an enhanced
vocabulary and improved model performance. Senkaiahliyan et al. [16] explored
GPT-4V’s potential in clinical education by assessing medical image
interpretation. Moreover, for automatic scoring tasks involving problem images
and textual context with rubrics, GPT-4V achieved a 51% accuracy rate for
science-based assessments [3]. Similarly, Gemini integration into products
like Google Bard and Pixel enhances reasoning, planning, and writing
capabilities. Its availability through the Gemini API (Google AI studio) makes
it a valuable resource for developers and enterprise customers, showcasing its
potential applicability in educational technology [12]. Overall, both GPT-4V
and Gemini represent significant strides in the field of AI and language
models. While they share certain multimodal capabilities, differences in
architecture, training methodologies, performance, and applications highlight
the diverse and evolving landscape of AI technology. Thus, an early
investigation into their utilization in the medical imaging domain will
provide a valuable reference study to achieve improvements and develop useful
MLLM-based Medical AI technology.

Figure 1: Workflow of the experiment. The pipeline shows the curation of lung
X-ray and retinal fundoscopy images, followed by prompt design through an
iterative process, and concluded using Gemini and GPT-4V to classify and
interpret the input images.

##  III Methodology

###  III-A Data Sources

In this work, we have used two medical image modalities: lung X-ray images and
retinal fundoscopy images. For both modalities, we use real and synthetic
samples. For synthetic lung X-ray images, this study reanalyzed the synthetic
images generated by Ali et al. [17] using neural diffusion models. In the
work, they employed a pre-trained DALLE2 model to produce lung X-ray and CT
images using a simple input text prompt. Furthermore, they trained a stable
diffusion model to generate lung X-ray images. For real X-ray images, we
utilized samples from the Kaggle dataset comprising 5,863 lung X-ray images
representing samples for Pneumonia and Normal cases [18]. For fundoscopy
images, we utilized images of the DRIVE dataset of retinal fundoscopy images
[19]. This dataset consists of retinopathy scans accumulated from 400 diabetic
subjects between 25-90 years of age. Synthetic images are generated from these
scans using the GPT-4V model. Overall, a set of 400 images was curated,
comprising 200 images for each modality with an equal distribution of real and
synthetic images. Fig. 1 represents the overall flow followed by this work.

###  III-B Prompt Design

After developing a pool of synthetic and real images, the next step is to
formulate an appropriate input prompt for the MLLMs that can effectively align
with our intended research task. For prompt design, we adopted the NERIF
method proposed by Lee and Zhai [3]. In NERIF, users initially draft a prompt
incorporating essential components pertinent to the task. Subsequently,
validation cases are employed to ascertain whether the prompt effectively
aligns with the user’s intended task. If discrepancies are identified, the
Notation-Enhanced Scoring Rubric is introduced or revised within the prompt.
This rubric amalgamates scoring rules derived from human experts, proficiency
level-aligned scoring criteria, and instructional notes to enhance scoring
accuracy. The validation and refinement process iterates until the machine’s
performance improvement reaches a plateau. In this work, two different input
prompts (one for classification and another for interpretation of the image)
are devised based on the nature of the task. Table I shows the input prompts
used in this work.

TABLE I: Input prompts formulated for the MLLMs Modality Type |  Task 1 |  Task 2
---|---|---
Lung X-ray |  Act like an experienced Radiologist. Classify the image into real or synthetic? |  Explain the image
Retina fundoscopy images |  Act as a trained Ophthalmologist. Do you think this input image is real or fake? |  Explain the image
TABLE II: Summary of results of Gemini and GPT-4V on the classification task
of real versus synthetic images.

Modality Type |  Model |  Real correcta |  Synthetic correctb |  Remaining (Incorrect or Not sure)c
---|---|---|---|---
Lung X-ray |  Gemini |  79 |  68 |  51
GPT-4V |  54 |  42 |  104
Retina fundoscopy images |  Gemini |  78 |  73 |  49
GPT-4V |  61 |  46 |  93

aReal correct: Real images identified correctly.
bSynthetic correct: Synthetic images identified correctly.
cAll remaining results are placed under the Incorrect or not sure category.

TABLE III: Comparison of results for Gemini versus GPT-4V versus Human. Only one randomly selected image for each class. Input image |  |  |  |
---|---|---|---|---
Original label |  Real |  Synthetic |  Real |  Synthetic
Gemini |  The image is real |  The image is real |  The image is real. |  The image is most likely fake.
This is a chest x-ray of a child. The heart is enlarged, and has fluid in lungs indicating heart failure. |  The image is of a chest X-ray. On the right side of the image, there is a large white area. |  In this fundus photograph, the retinal vessels are dilated and tortuous, and has vitreous hemorrhage. |  This image may be fake, such as the regularity of the blood vessels and the lack of any other details in the image.
GPT-4V |  Based on the content of the image provided, it is a real X-ray image of a human chest. |  Sorry, I can’t help with identifying or making assumptions about whether an image is real or fabricated. |  Sorry, I can’t help with identifying or making assumptions about whether medical images are real or fake. |  I’m sorry, I cannot provide real-time analysis for images. However, it appears to be a real fundus photograph.
The image you’ve provided appears to be a medical radiograph, commonly known as an X-ray image, of a human chest. |  In a chest X-ray like this, bones such as ribs and spine will appear white because it absorb more X-ray radiation. |  This fundus image is the interior surface of the eye includes the retina, optic disc, macula, fovea, and posterior pole. |  This image appears to be a close-up photograph of a human eye’s interior, showcasing the retina.
Doctor |  I think it’s a real image. |  Fake |  It’s a real fundoscopic image of the eye |  AI generated image
It’s an AP view. Hilar congestion: suggestive of possible infection. |  I”s an AI generated image as it doesn’t show air bronchogram, with poor lung and cardiac margins. |  It’s a real fundoscopic image of the eye, Normal appearance of the fundus. |  It’s an AI generated picture of retina.
Figure 2: Performance analysis of the MLLMs and medical practitioner. The
Gemini outperformed the GPT-4V in both image classification and
interpretation.

###  III-C Evaluation

We provided the samples of real and synthetic images to Gemini and GPT-4V and
recorded the classification results. Following this, we adopted a qualitative
evaluation approach and presented randomly selected images along with the
interpretation of the MLLMs to a trained doctor, who volunteered to help us
label the images and provide brief interpretation. In the interest of time, we
kept the number small and could only get twelve evaluations from the doctor.

##  IV Results and Discussions

The classification results are summarized in Table II. From the experimental
results, it is evident that Gemini leads in the performance of classifying
images (both real and synthetic images). It is worth mentioning that Gemini
provided more confident output labels for the images. However, occasionally,
there are instances where Gemini predicts the label using a less confident
tone, for example, “The image is likely to be real.” or “The image is of an
eye with a large retinal detachment. It looks real, but I cannot be 100%
certain.”. Among 100 real lung X-ray images, Gemini correctly identified 88
images, while GPT-4V correctly identified 54 images only. Interestingly, most
of the time, the GPT-4V responded with “Sorry, I can’t help with identifying
or making assumptions about medical images”. This can be a possible reason for
the lower classification rates.

Table III shows the interpretation generated by both the MLLMs and the ground
truth labels. It does seem that the Gemini could provide concise and confident
remarks. In comparison, GPT-4V generated generic information (generic
explanation of X-ray imaging and structure, etc.). Moreover, the Gemini’s
interpretations were comparatively more aligned with the medical practitioner
evaluations.

The overall performance of the GPT-4V versus Gemini is summarized in Figure 2,
in comparison with the labels assigned by the medical practitioner. From the
figure, it is evident that the classification labels by Gemini are in close
agreement with the labels assigned by the medical professional. Similarly, the
number of uncertain output labels is smaller for Gemini than for GPT-4V. In
comparison, the GPT-4V most often responded as uncertain about the labels. The
overall accuracy for the Gemini is 68.5% in x-ray images classification and
73.3% in fundoscopy images classification. On the other GPT-4V showed less
than 50% for both x-ray and fundoscopy images classification analysis.

Limitations: It is important to notice that both Gemini and GPT-4V
occasionally refuse to process the input images, confusing the input image
data for illicit contents with the phenomena more commonly experienced with
Gemini. While this work presents a useful benchmark study on the potential of
MLLMs in medical imaging, we acknowledge that the investigations presented in
this work are not exhaustive and may not comprehensively address all facets
inherent in the analysis of input images, as the number of lung X-ray images
and retinal images is too small. Our experiments involve the efforts of a
single doctor who agreed to contribute to this work voluntarily. Hence, human
bias in the interpretation cannot be ruled out. Thus, it is imperative to
acknowledge the inherent limitations associated with the scope of our
experiments, as they may not encapsulate the entirety of considerations
relevant to diverse dimensions of the input imagery. It is essential to
recognize that the current study’s reach may be constrained, and caution
should be exercised in generalizing the interpretations of the Gemini and
GPT-4V models to broader contexts. In short, we do not advise extrapolation of
these results to diverse scenarios in the field of medical imaging.

Additionally, our analysis did not include the evaluation of long-form
question answering, a critical aspect highlighted in the literature within the
context of MedPaLM [20] and MedPaLM 2 [21]. Future research could probe the
effectiveness of MLLMs in handling more extensive and complex medical queries
encountered in real-world medical literature and examinations.

Moreover, real-time data and advanced techniques like retrieval-augmented
generation (RAG) offer another avenue for improving model performance. These
methodologies could significantly enhance the accuracy and reliability of
MLLMs in medical contexts by providing them with the most up-to-date
information and enabling them to draw from a wider range of sources.

In conclusion, our study provides valuable insights into the capabilities and
limitations of Gemini and GPT-4V within the medical imaging domain,
particularly in ophthalmology and pulmonary disease studies. However, it also
highlights several areas for future research. By addressing these limitations,
future work can not only enhance our understanding of MLLMs’ potential but
also drive the development of more sophisticated and effective AI tools for
medical diagnostics and treatment.

##  V Conclusion and Recommendations

This research presented a comprehensive comparison between Gemini and GPT-4V
for medical image analysis, specifically focusing on their potential in
classifying retinal and lung X-ray images, including both real and synthetic
images, and providing interpretations as well. Throughout our experimental
evaluations, it became evident that Gemini consistently outperforms GPT-4V in
terms of classification accuracy and interpretation quality. Particularly
noteworthy is Gemini’s ability to provide interpretations that closely align
with those of medical practitioners. Conversely, GPT-4V exhibited limited
capabilities, often generating generic responses to input. The findings
highlight Gemini’s superior performance in image classification and its
effectiveness in extracting complex information from the input medical images.
Apart from the inherent limitations in the design of this study, the results
reflect an early promising potential of MLLMs in medical image analysis
applications. These findings significantly contribute to the body of
literature on AI in healthcare, suggesting the need for more sophisticated and
context-aware AI tools. Furthermore, this study advances our understanding of
AI’s role in identifying synthetic images while offering valuable insights for
ophthalmology and pulmonary disease diagnosis. It provides important
directions for future research and development in this rapidly evolving field.

## Acknowledgment

Open Access funding provided by the Qatar National Library.

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# Generating Summaries for Scientific Paper Review

Ana-Sabina Uban
Faculty of Mathematics and Computer Science,
University of Bucharest
<EMAIL_ADDRESS>
Cornelia Caragea
Computer Science,
University of Illinois at Chicago
<EMAIL_ADDRESS>

###### Abstract

The review process is essential to ensure the quality of publications.
Recently, the increase of submissions for top venues in machine learning and
NLP has caused a problem of excessive burden on reviewers and has often caused
concerns regarding how this may not only overload reviewers, but also may
affect the quality of the reviews. An automatic system for assisting with the
reviewing process could be a solution for ameliorating the problem. In this
paper, we explore automatic review summary generation for scientific papers.
We posit that neural language models have the potential to be valuable
candidates for this task. In order to test this hypothesis, we release a new
dataset of scientific papers and their reviews, collected from papers
published in the NeurIPS conference from 2013 to 2020. We evaluate state of
the art neural summarization models, present initial results on the
feasibility of automatic review summary generation, and propose directions for
the future.

##  1 Introduction

Reviewing is at the center of the scientific publication process, and the
quality of publications is dependent on it. In many scientific fields,
including natural language processing and machine learning, submissions for
publication are reviewed using a peer review system. Recently, these fields
are seeing increasing volumes of submissions each year, especially in high
reputation venues. This has created an issue of over-burdening of reviewers,
which is not only a problem for the quality of life of scientists, but also
consequently affects the quality of the reviews. With ever increasing volume
of new results in these fields, submissions for publication are expected to
multiply still, and the problem is only expected to deepen, which is raising
concerns in the scientific community Rogers and Augenstein (2020).

One avenue for ameliorating this problem is relying on artificial intelligence
to assist with the process, in order to remove some of the burden from the
human reviewers. A possibility would be to generate reviews or article
summaries automatically, in order to speed up the human’s understanding of the
paper, or to assist with parts of the review writing, e.g., a few sentences
summary.

Text generation has seen impressive improvements in recent years, being one of
the most active fields in NLP, with the highest leaps in performance of newly
published models. Models such as BERT Devlin et al. (2019), GPT-3 Brown et al.
(2020) have shown impressive results for text generation, as well as for other
tasks, acting as language models which can generalize for a wide range of
tasks in NLP with only little fine-tuning.

Text summarization is a problem of text generation. Depending on the approach,
summarization can be extractive Zheng and Lapata (2019) or abstractive See et
al. (2017); Nallapati et al. (2016). Extractive summarization is performed by
selecting key sentences from the original text, while abstractive
summarization tackles the more difficult problem of generating novel text that
summarizes a given input—the problem we are interested in and explore in this
paper. As for text generation in general, state-of-the-art models for
summarization are generally neural and transformer-based such as PEGASUS Zhang
et al. (2020) and Prophet Qi et al. (2020). These models have been used for
text summarization for different domains, including news Desai et al. (2020)
and scientific texts. For scientific text summarization, Zhang et al. (2020)
have obtained best results in existing literature, based on evaluation on a
dataset of articles published on arXiv and PubMed using papers’ abstracts as
ground truth.

Scientific texts pose specific problems for summarization, given their
particular structure and way of organizing information. This is why the
problem of scientific text summarization has been approached separately from
general summarization systems. The problem of scientific text summarization
has been approached before Yasunaga et al. (2019); Altmami and Menai (2020);
Ju et al. (2020); Cohan and Goharian (2017); Qazvinian et al. (2010). Top
conferences in NLP have organized workshops on scholarly document processing,
including shared tasks specifically focused on scientific document
summarization Chandrasekaran et al. (2019). Most approaches for scientific
text summarization use an extractive Saggion and Lapalme (2000); Saggion
(2011); Yang et al. (2016); Slamet et al. (2018); Agrawal et al. (2019); Hoang
and Kan (2010) or citation-based approach Cohan and Goharian (2017); Qazvinian
et al. (2010); Ronzano and Saggion (2016), with a few exceptions attempting
abstractive summarization on scientific texts Lloret et al. (2013). Notably,
Ju et al. (2020) use a combined extractive and abstractive approach based on
BERT. Sun and Zhuge (2018) propose an approach based on semantic link networks
for summarizing scientific texts. A recently published survey Altmami and
Menai (2020) contains a more exhaustive overview of previous attempts at
summarizing scientific papers.

Given the excellent results of recent text generation models, it is promising
to consider new applications in fields where they have not been leveraged in
practice before. We propose that one such task is scientific review summary
generation. We evaluate in this paper the feasibility of automatically
generating review summaries for scientific papers. We use state-of-the-art
models for text summarization, and apply them to our problem. We release a
dataset of articles and reviews from NeurIPS, which we use to assess the
performance of automatic summarization models for the problem of review
summary generation.

##  2 Dataset

We build a dataset of articles and associated reviews by scraping NeurIPS’s
conference website,111https://papers.neurips.cc and collecting all articles
published in NeurIPS between 2013 and 2020, along with their reviews. To
obtain the full text of the papers, we downloaded the PDFs from the website
and extracted the text using Grobid.222https://github.com/kermitt2/grobid
Reviews were extracted directly from the HTML content of the web pages, and,
where needed, heuristics were used in order to exclude the texts of the
author’s responses. Each article can have several reviews. Table 1 summarizes
statistics about the dataset.

Articles | 5,950
---|---
Reviews | 18,926
Avg review len (words) | 399
Avg review len (sentences) | 21
Avg abstract len (words) | 159
Avg abstract len (sentences) | 7
Table 1: Dataset statistics.

##  3 Summarization Experiments

Reviews of scientific articles are usually comprised of a short summary,
followed by the comments comprising the reviewer’s evaluation of the article,
mentioning its strengths and its weaknesses. The initial summary of the paper
is usually a short objective description of its contents, so in theory it
could be inferred solely based on the article’s content. Based on this
premise, we formulate the problem of automatic review generation as a text
summarization problem.

| R-1 | R-2 | R-L | BERTScore
---|---|---|---|---
vs. arXiv abstracts Zhang et al. (2020) | .447 | .173 | .258 | -
vs. abstract (NeurIPS) | .236 | .046 | .151 | .793
vs. review summaries (individual whole) | .169 | .023 | .117 | .789
vs. review summaries (concatenated whole) | .206 | .033 | .127 | .784
Table 2: Performance of pretrained model

Pre-processing. We aim to separate the two different parts of each review: the
initial part containing a short summary of the paper, from the following
comments and evaluation of the paper. A manual inspection of extracted reviews
in our dataset for papers up to 2019 shows that many reviews include replies
to author responses from the rebuttal phase of the review, and these can be
found either in the beginning or end of the review, without a consistent
pattern, sometimes separated from the main review by ASCII separators (strings
of "-"/"="/" "). We then rely on heuristics in order to correctly extract the
summary part of the review, by searching the review text for keywords such as
"rebuttal" or "response": if these are found at the beginning of the review,
we then look for ASCII separator characters, and consider the original review
to begin after the separator; otherwise, we assume the summary is found at the
beginning of the review. For papers from NeurIPS 2020, the different sections
of the review are clearly marked (summary, strengths, weaknesses, clarity and
correctness), so this pre-processing step was not needed. After this step, we
split the obtained text into sentences and select the first $k$ sentences as
the summary. Our motivation in doing so was driven by several works on extreme
classification Narayan et al. (2019, 2018) aimed at generating short, one-
sentence news summary to answer the question: “What is the article about?”.

| R-1 | R-2 | R-L | BERTScore
---|---|---|---|---
vs. abstract (NeurIPS) | .261 | .034 | .141 | .812
vs. review summaries (indiviual whole) | .230 | .031 | .148 | .817
vs. review summaries (concatenated whole) | .254 | .046 | .145 | .806
vs. review summaries (concatenated 5 sents) | .273 | .047 | .155 | .808
vs. review summaries (concatenated 4 sents) | .279 | .046 | .158 | .810
vs. review summaries (concatenated 3 sents) | .287 | .045 | .164 | .813
vs. review summaries (concatenated 2 sents) | .290 | .042 | .170 | .817
vs. review summaries (concatenated 1 sent) | .246 | .032 | .160 | .821
vs. review summaries (individual 5 sents) | .227 | .030 | .149 | .818
vs. review summaries (individual 4 sents) | .220 | .028 | .147 | .819
vs. review summaries (individual 3 sents) | .207 | .026 | .117 | .819
vs. review summaries (individual 2 sents) | .176 | .022 | .127 | .820
vs. review summaries (individual 1 sent) | ,114 | .053 | .091 | .822
Table 3: Performance of fine-tuned model on abstract and review summary

Model. Language modeling in NLP has recently seen great advancements, and is
one of the most active areas of research in NLP, with new results being
published every few months. The best performing models are based on neural
architectures, among which transformers play an important role. Text
summarization in particular is a type of text generation problem; the current
state of the art in text generation is PEGASUS Zhang et al. (2020), which is a
transformers-based model trained to generate summaries by masking important
sentences in a source text. PEGASUS obtained state-of-the-art results in text
summarization across 12 different datasets in different domains, including
scientific texts.

We experiment with using PEGASUS in order to generate summaries of scientific
articles in our dataset, and assess its performance compared to the collected
reviews.

Model pre-trained on abstracts. We first experiment with a pre-trained version
of PEGASUS for scientific text summarization, which was trained to generate
abstracts of scientific texts based on a dataset of arXiv articles Cohan et
al. (2018). In order to ensure no overlap between the test set used for
evaluation in our experiments and the articles in the arXiv database used in
pre-training of the model, we select as our test set only the articles in our
dataset published in 2020 (the arXiv dataset was published in 2018) - we use
1000 of these articles as our test set and keep the rest of 898 as a
validation set. The 2020 reviews are also the highest-quality of our dataset,
since the summary section of the review is clearly marked and used as is for
evaluation (as opposed to extracted based on heuristics).

Model fine-tuned on reviews. Second, we attempt to generate paper summaries
which best approximate a review. For this purpose, we fine-tune the pre-
trained model used in the previous experiment on our own data, using as
targets the reviews in our dataset. As a training set, we use the articles and
reviews in our dataset published before 2020. While our dataset is smaller
than the arXiv dataset used for the pre-trained model, it is expected to be
similar to the original training data. For each article, one review is
selected at random and used as ground truth for training the summarization
model. The training set contains 4,052 papers and their reviews.

Evaluation. We evaluate the models using the ROUGE metric, and compare the
generated summaries both to the abstract and the reviews. We report ROUGE-1,
ROUGE-2 and ROUGE-L, as well as BERTScore, using the RoBERTa-large
model333roberta-large_L17_no-idf_version=0.3.9 (hug_trans=4.2.2) Zhang et al.
(2019). Our setup can be evaluated on multiple labels for the same input text:
in our test set, one paper can have several reviews. We evaluate our models
with multiple labels: first by considering them separately as independent
examples, and second by concatenating all reviews for a given input article
into one single reference text, and evaluating against it.

We show examples of generated reviews using our model, along with the original
reviews for the same article, in the Appendix.

Results. We report separately the results of the pre-trained and the fine-
tuned model. We compare different setups, using as target texts both the
abstracts and the reviews. In the case of the reviews, we consider separately
as a target test the whole review or only the summary section, varying the
number of extracted sentences from 1 to 5, and experiment with the two
evaluation setups: concatenating the different reviews corresponding to one
article, or considering them as separate test examples.

Tables 2 and 3 and show the results for all setups. The pre-trained model
obtains better results when evaluated against abstracts than against reviews,
across configurations and metrics. Although the pre-trained model was trained
to generate abstracts, the fine-tuned model still obtains slightly better
results compared to abstracts, suggesting it might solve a relevant domain
adaptation aspect. The fine-tuned model also shows improved results for review
summary generation. In terms of ROUGE scores, the optimal number of sentences
of the summary extracted from the review summary seems to be 2 in the
concatenated setup, while in the individual setup, the performance increases
with the number of sentences. BERTScore strictly decreases with the number of
sentences for both setups. Especially in the concatenated setup, using the
first 1-2 sentences in the review summary as labels out-performs evaluating
against the full review summary, suggesting that the generated summaries
generally contain information present in the beginning of the review.

| R-1 | R-2 | R-L | BERT
---|---|---|---|---
|  |  |  | Score
vs. full review (concat) | .152 | .036 | .092 | .803
vs. full review (individual) | .241 | .040 | .139 | .806
vs. strenghts (concat) | .270 | .039 | .159 | .815
vs. strengths (individual) | .200 | .038 | .135 | .820
vs. weaknesses (concat) | .232 | .028 | .134 | .803
vs. weaknesses (individual) | .212 | .027 | .134 | .808

Table 4: Performance of fine-tuned model on full review and other review
sections

###  3.1 Feasibility of Generating Full Reviews

The fine-tuned model is better at generating review summaries than the pre-
trained model, across setups.

The generation of a full review, including critical interpretations from the
reviewers, is a much more challenging problem than generating paper summaries.
In order to assess how well a summarization model can approximate a full
review, including not only the summary, but also the critical comments
sections, we separately evaluate our model using the full reviews as targets,
as well as against the separate sections (we consider the Strengths and
Weaknesses sections), as show in Table 4. We notice that the performance is
generally lower than for the review summary, but still comparable. The
Strengths section seems to have the most in common with the review summary
according the better results.

##  4 Conclusions

We have formulated the problem of scientific text review generation, as a
novel task in NLP with practical applications for the scientific community.
Review generation is related to the text summarization task, but has its own
specific features, which is what makes it a difficult problem to solve. We
have taken the first steps towards building an automatic system for review
generation; and have collected and are releasing a dataset of scientific
articles and reviews which can be used for future experimentation into the
topic.

We conclude that scientific review generation is a difficult problem, with
current performance considerably below that of state-of-the-art text
generation models on scientific abstracts. Nevertheless, the small
improvements in performance we obtain through fine-tuning the model suggest
that the problem might be approachable, and encourage us to continue to study
it. We propose that more training data could be useful to obtain better
results, as would a more accurate extraction of the summary section of the
review. In the future, we would like to explore a more complex training
strategy in order to improve performance, such as multi-task learning (to
jointly train the model to generate reviews and abstracts), or conditional
text generation, in order to constrain the model to generate review-like
texts, while keeping the content relevant to the source article.

##  5 Ethical Considerations

Our dataset poses no privacy issues. With regards to the task of paper review
generation, it is unclear if generating reviews entirely automatically is
desirable from a practical as well as ethical perspective. Instead, we
approach the problem summary generation for reviews, in view of a possible
computer-assisted process for review generation, which would not exclude
humans. We think a computational tool for assisting with the ever-growing
burden of reviewing can help the community and eventually lead to higher
quality reviews, and hope our paper can encourage discussion on the topic. We
leave open to discussion the question of how such a tool could best be
integrated in the current review system.

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##  Appendix A Appendix

Generated review |  Original review
---|---
thanks to the authors for a very interesting paper. the main contribution that the authors have been able to solve this problem is the transient spike packet model, which can take into account discrete populations of neurons in a very long time invariant networks, and as the authors argue allows for a much better signal transmission and/or slower responses to fluctuations in the brain at all times. this implies that the authors argue allows for a much better model of nontriviality invariant networks, independent of population sizes and/or slower responses to fluctuations in the brain at all times. (…) |  This paper uses the Drosophila2̆019s olfactory system (ORN to PN to LHN) as an example to demonstrate how the heterogeneity in the intrinsic parameters of single neurons could benefit for information transmission in a feedforward network, which is the main conceptual contribution of this work. Then they extend the idea of heterogeneous neurons to multiple layers of feedforward networks. (…)

Table 5: Example generated review, and actual review, for article titled
"Lamina-specific neuronal properties promote robust, stable signal propagation
in feedforward networks".

Generated summary |  Original abstract
---|---
methods for machine- learning models play an important role in developing, and using predictive models as information on what features were important for a given output for any machine- learning model, enabling us to better understand, validate, and interpret model decisions. $<n>$ researchers have developed a number of attribution methods that provide estimates of importance of input features towards model output for specific types of models, and accurate feature estimates for any machine- learning model. $<n>$ we present a new approach to estimating feature importance for any machine- learning model using causal explanation. $<n>$ this approach can be applied to any machine- learning model since it does not require retraining or adapting the original model to demonstrate importance scores are strongly causal, provided another machine- learning objective to train a model uses test data, and another machine- learning objective to learn accurate output. $<n>$ experiments show that the uncertainty estimates provided by this methodology are significantly more accurate than existing model-agnostic methods, and that the uncertainty scores provided by this methodology are significantly more accurate than existing model-agnostic methods. $<n>$ this approach can be applied to any machine- learning model since it does not require retraining or adapting the original model to demonstrate importance scores are strongly causal |  Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models. However, providing fast and accurate estimates of feature importance for high-dimensional data, and quantifying the uncertainty of such estimates remain open challenges. Here, we frame the task of providing explanations for the decisions of machine-learning models as a causal learning task, and train causal explanation (CXPlain) models that learn to estimate to what degree certain inputs cause outputs in another machine-learning model. CXPlain can, once trained, be used to explain the target model in little time, and enables the quantification of the uncertainty associated with its feature importance estimates via bootstrap ensembling. We present experiments that demonstrate that CXPlain is significantly more accurate and faster than existing model-agnostic methods for estimating feature importance. In addition, we confirm that the uncertainty estimates provided by CXPlain ensembles are strongly correlated with their ability to accurately estimate feature importance on held-out data.

Table 6: Example of generated summary and original abstract, using pretrained
model (paper title "CXPlain: Causal Explanations for Model Interpretation
under Uncertainty")

Generated summary |  Original review
---|---
neural networks are extensively used today in safety critical control systems such as autonomous vehicles and airborne collision avoidance systems. $<n>$ rigorous methods to ensure correct functioning of neural network controlled systems is imperative. $<n>$ verification refers to a broad class of techniques that provide strong guarantees of correctness by exhibiting a proof of abstraction. $<n>$ verification of neural networks has attracted lot of attention in recent years. $<n>$ verification of neural networks has attracted a lot of attention in recent years. $<n>$ this paper focuses on neural networks with rectified linear unit as range of activation function and we investigate the output range problem for feed-forward neural networks with rectified linear unit as range of activation function. $<n>$ broad objective is to investigate techniques to verify neural network controlled physical systems such as autonomous vehicles. $<n>$ verification refers to a broad class of techniques that provide strong guarantees of correctness by exhibiting a proof of abstraction. $<n>$ important verification problem is that safety, wherein one seeks to ensure that the neural network controlled system never reaches an unsafe set of states. $<n>$ important computation is to compute the output of network controller given a set of input valuations. $<n>$ we focus on neural networks with rectified linear unit as range of activation function and we investigate the output range |  First of all, my knowledge of formal verification of neural networks is very limited, and I apologize for the limitations this poses on my review. That said, I found this paper very interesting, well written, and from my limited understanding of the literature, this seems like a novel and highly useful tool in the toolbox for verifying neural network models. I am strongly in favor of acceptance. My main questions are the following: * It is not clear to me what increase in false positives does the method introduce by relaxing the estimate of the output of the network to a superset. * I would like to see a more formal definition of the algorithm with the m̈oving pieces(̈e.g. partitioning strategies) stated more explicitly. Then I would like to have a discussion of the considerations that go into defining these m̈oving pieces.̈ * What are the practical limitations of the method on real-world network sizes and architectures.
|  Overall, I like the approach in the paper and the theoretical background
looks solid (though I didn’t check the proofs). My main problem with a paper
is in the experimental part: * just one experiment is considered on rather toy
neural network * no comparison with other methods is made Thus, it is
impossible to evaluate the usefulness of the proposed method.
|  Pros: 1. Paper is well written and easily readable. 2. It presents a novel
approach for state space reduction of neural network that could be extended to
similar problems. 3. It address an important issue of computational complexity
in output range analysis for neural network. Cons: 1. This paper should
explore different partitioning schemes and provide a timing comparison among
them. 2. It should provide a metric to compare degree of over approximation as
compared with approach in [4]
Table 7: Example of generated summary and original reviews, using pre-trained
model (paper title "Abstraction based Output Range Analysis for Neural
Networks")