Read the following text on the challenges of automatic text summarization and summarize the key points:

Automatic text summarization, the process of using algorithms and computational techniques to generate concise summaries of lengthy documents or collections of text, has been an active area of research in natural language processing (NLP) for decades. While significant progress has been made, several challenges remain that hinder the widespread adoption and performance of automatic summarization systems.

One of the primary challenges is the inherent complexity of human language and the nuances involved in understanding and interpreting text. Summarization requires a deep comprehension of the content, including the ability to identify and extract the most salient information, while preserving the overall meaning and context. This task becomes even more complex when dealing with ambiguous, figurative, or domain-specific language.

Another significant challenge is the lack of consistent evaluation metrics for assessing the quality of automatically generated summaries. While several metrics exist, such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy), they often fail to capture the nuances of human-like summarization and may not align well with human judgments of summary quality.

Furthermore, the availability of high-quality labeled data for training summarization models is limited. Creating large datasets of document-summary pairs requires substantial human effort and expertise, which can be time-consuming and expensive. This scarcity of training data can limit the performance and generalizability of machine learning models for summarization.

Additionally, different summarization tasks may require different approaches and techniques. For example, generating summaries for news articles may involve different strategies compared to summarizing scientific papers or legal documents, each with their own domain-specific language and structural considerations.

As the field of automatic text summarization continues to evolve, researchers are exploring various techniques, including deep learning models, attention mechanisms, and hybrid approaches that combine extractive and abstractive methods. Overcoming these challenges will be crucial for developing more robust and effective summarization systems that can assist in information management and knowledge dissemination across various domains.
Read the following text on the challenges of automatic text summarization and summarize the key points:

Automatic text summarization, the process of using algorithms and computational techniques to generate concise summaries of lengthy documents or collections of text, has been an active area of research in natural language processing (NLP) for decades. While significant progress has been made, several challenges remain that hinder the widespread adoption and performance of automatic summarization systems.

One of the primary challenges is the inherent complexity of human language and the nuances involved in understanding and interpreting text. Summarization requires a deep comprehension of the content, including the ability to identify and extract the most salient information, while preserving the overall meaning and context. This task becomes even more complex when dealing with ambiguous, figurative, or domain-specific language.

Another significant challenge is the lack of consistent evaluation metrics for assessing the quality of automatically generated summaries. While several metrics exist, such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy), they often fail to capture the nuances of human-like summarization and may not align well with human judgments of summary quality.

Furthermore, the availability of high-quality labeled data for training summarization models is limited. Creating large datasets of document-summary pairs requires substantial human effort and expertise, which can be time-consuming and expensive. This scarcity of training data can limit the performance and generalizability of machine learning models for summarization.

Additionally, different summarization tasks may require different approaches and techniques. For example, generating summaries for news articles may involve different strategies compared to summarizing scientific papers or legal documents, each with their own domain-specific language and structural considerations.

As the field of automatic text summarization continues to evolve, researchers are exploring various techniques, including deep learning models, attention mechanisms, and hybrid approaches that combine extractive and abstractive methods. Overcoming these challenges will be crucial for developing more robust and effective summarization systems that can assist in information management and knowledge dissemination across various domains.
user
The text discusses the challenges faced in automatic text summarization, including the complexity of understanding natural language, lack of consistent evaluation metrics, limited availability of high-quality training data, and the varying requirements of different summarization tasks. It highlights the need to overcome these obstacles through ongoing research and exploration of techniques like deep learning, attention mechanisms, and hybrid approaches.
assistant
Generate a series of (instruction, response) pairs that are similar in context and structure to the example provided below. Each pair should consist of a concise instruction followed by an appropriate, detailed response. The instruction should pose a clear task or question, while the response should provide a comprehensive answer or solution that could be understood by someone with a basic understanding of the subject.

Example pair: 
Instruction: Please read this text: 

People consume information in lots of different ways: visually, audibly, through text, and sometimes all three at once. Search engine algorithms like Google’s are advancing to reflect this experience for users. No longer will the algorithms just look at web pages as a single piece of text content. Google will look at all the ways real people consume information: text, images, videos, infographics, tables, charts, and interactive elements that make up a piece of content.

In recent years, we saw the rise of visual search – particularly in image-heavy industries like travel and tourism or ecommerce. Moving into 2023, Google will expand its foothold in multisearch and multisearch near me. 

What does it say about multi-format content in a brief summary?
Response: The text says that people prefer content in different forms like audio, video, or text format. As a result, Google plans to advance the multi-search function and page search algorithms accordingly. 

Your task is to generate more pairs that maintain this level of clarity and detail. The topic is Summarization. Write a long text of instruction by yourself, then summarize the given instruction in a response. Ensure that the responses are informative and accurate, suitable for an educational context. 

Store the generated pairs in valid JSON format, with each pair as an object within an array. Each object should have two key-value pairs: "instruction" and "response". For instance: 

{"contents": [{"instruction": "text", "response": "text"}, {"instruction": "text", "response": "text"}, ...]} 

Remember to maintain consistency in the format and ensure the generated pairs are diverse and cover a broad range of subjects. You must return the response in the asked format and you must not add any additional text in your response.
