Summarize the main points from this passage about challenges and future directions in text summarization research:

While significant progress has been made in text summarization techniques, several challenges remain to be addressed. One major challenge is the ability to generate abstractive summaries that are not only fluent and coherent but also factually consistent with the input text. Current models often hallucinate or introduce factual inconsistencies, which can be problematic in critical domains like news reporting or scientific literature.

Another challenge lies in summarizing long, multi-topic documents or collections of related texts. Existing models often struggle to capture the diverse themes and discourse structures present in such documents, leading to summaries that lack comprehensiveness or miss important details.

Furthermore, summarization models often lack adaptability to different domains and genres, as they are typically trained on general-purpose corpora. Developing techniques to effectively transfer knowledge from pre-trained models and fine-tune them for specific domains remains an active area of research.

Future directions in text summarization research include exploring more advanced architectures that can better capture long-range dependencies and discourse structures, incorporating external knowledge sources and commonsense reasoning capabilities, and developing robust evaluation metrics that go beyond surface-level measures to assess higher-level qualities like coherence and faithfulness.
Summarize the main points from this passage about challenges and future directions in text summarization research:

While significant progress has been made in text summarization techniques, several challenges remain to be addressed. One major challenge is the ability to generate abstractive summaries that are not only fluent and coherent but also factually consistent with the input text. Current models often hallucinate or introduce factual inconsistencies, which can be problematic in critical domains like news reporting or scientific literature.

Another challenge lies in summarizing long, multi-topic documents or collections of related texts. Existing models often struggle to capture the diverse themes and discourse structures present in such documents, leading to summaries that lack comprehensiveness or miss important details.

Furthermore, summarization models often lack adaptability to different domains and genres, as they are typically trained on general-purpose corpora. Developing techniques to effectively transfer knowledge from pre-trained models and fine-tune them for specific domains remains an active area of research.

Future directions in text summarization research include exploring more advanced architectures that can better capture long-range dependencies and discourse structures, incorporating external knowledge sources and commonsense reasoning capabilities, and developing robust evaluation metrics that go beyond surface-level measures to assess higher-level qualities like coherence and faithfulness.
user
The passage discusses several challenges and future directions in text summarization research. Key challenges mentioned include generating factually consistent abstractive summaries without hallucinations, summarizing long multi-topic documents while capturing diverse themes and discourse structures, and adapting models to different domains and genres. Future research directions proposed include advanced architectures for capturing long-range dependencies and discourse, incorporating external knowledge and commonsense reasoning, and developing robust evaluation metrics that assess higher-level qualities like coherence and faithfulness beyond surface-level measures.
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: Condense this film info for me into only two sentences: 

What is this film about?
The official synopsis for "Guardians of the Galaxy Vol. 3" from Marvel Studios reads:


"Peter Quill, still reeling from the loss of Gamora, must rally his team around him to defend the universe along with protecting one of their own. A mission that, if not completed successfully, could quite possibly lead to the end of the Guardians as we know them."

As evidenced from the film's trailers, it seems as though the Guardians will be protecting Rocket from the High Evolutionary, as the two of them apparently have an intense backstory.

PHOTO: Rocket (voiced by Bradley Cooper) is shown in Marvel Studios' Guardians of the Galaxy Vol. 3.
Rocket (voiced by Bradley Cooper) is shown in Marvel Studios' Guardians of the Galaxy Vol. 3.
Courtesy of Marvel Studios
Cooper confirmed as much when speaking to "GMA" at the film's premiere, saying, "Honestly, it was very emotional to play [Rocket]."

MORE: Bradley Cooper reveals what playing Rocket meant to him in 'Guardians of the Galaxy'
"You really get a chance to understand how he came to be," Cooper continued. "Rocket went through a lot."

PHOTO: Chukwudi Iwuji appears as The High Evolutionary in Marvel Studios' Guardians of the Galaxy Vol. 3.
Chukwudi Iwuji appears as The High Evolutionary in Marvel Studios' Guardians of the Galaxy Vol. 3.
Courtesy of Marvel Studios
Iwuji teased the High Evolutionary's villainy, saying, "He is such a great villain to play. I had so much fun playing him."

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"He's bad ... he's really bad," he added. "He really does do some horrible things, but it's a joy to play those [roles]."
Response: Guardians of the Galaxy Vol. 3 focuses on Peter Quill, who is still getting over Gamora's death, as he and the others protect Rocket from the High Evolutionary. You get to see how Rocket came to be and a great villain who does horrible things. 

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.
