Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles
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Abstract (arXiv)
Previous research in multi-document news summarization has typically concentrated on collating information that all sources agree upon. However, the summarization of diverse information dispersed across multiple articles about an event remains underexplored. In this paper, we propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event. To facilitate this task, we outlined a data collection schema for identifying diverse information and curated a dataset named DiverseSumm. The dataset includes 245 news stories, with each story comprising 10 news articles and paired with a human-validated reference. Next, to enable consistent automatic evaluation, we conducted a comprehensive analysis to pinpoint the position and verbosity biases when utilizing Large Language Model (LLM)-based metrics for evaluating the coverage and faithfulness of summaries. Through correlation analyses, we outline the best practices for effectively using automatic LLM-based metrics on the DiverseSumm dataset. Finally, we study how LLMs summarize multiple news articles by analyzing which type of diverse information LLMs are capable of identifying. Our analyses suggest that despite the extraordinary capabilities of LLMs in single-document summarization, the proposed task remains a complex challenge for them mainly due to their limited coverage, with GPT-4 only able to cover under 40% of the diverse information on average.
Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 17 Sep 2023.
Builds on
26 of 50 references.
References · 26
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- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
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- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
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- 2023Large Language Models are not Fair Evaluatorscited by 1,050
- 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
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- 2022Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluationcited by 141
- 2018Ranking Sentences for Extractive Summarization with Reinforcement Learningcited by 128
- 2021CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarizationcited by 120
14 more
- 2016Neural Summarization by Extracting Sentences and Wordscited by 114
- 2020Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articlescited by 96
- 2023LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarizationcited by 96
- 2021MS2: Multi-Document Summarization of Medical Studiescited by 80
- 2020A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portalcited by 72
- 2021CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuningcited by 44
- 2022Prompted Opinion Summarization with GPT-3.5cited by 44
- 2023Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluationcited by 14
- 2019Guiding Extractive Summarization with Question-Answering Rewardscited by 13
- 2016Multi-document abstractive summarization using ILP based multi-sentence compressioncited by 11
- 2022Discord Questions: A Computational Approach To Diversity Analysis in News Coveragecited by 11
- 2023SWING: Balancing Coverage and Faithfulness for Dialogue Summarizationcited by 10
- 2018Towards a Neural Network Approach to Abstractive Multi-Document Summarizationcited by 6
- 2023Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questionscited by 3
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Citing papers per year: 2023: 5; 2024: 17; 2025: 17; 2026: 6.
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- 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathy12 Apr 2026
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- 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarks3 Nov 2025
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