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Previous research in multi-document news summarization has typicallyconcentrated on collating information that all sources agree upon. However, toour knowledge, the summarization of diverse information dispersed acrossmultiple articles about an event has not been previously investigated. Thelatter imposes a different set of challenges for a summarization model. In thispaper, we propose a new task of summarizing diverse information encountered inmultiple news articles encompassing the same event. To facilitate this task, weoutlined a data collection schema for identifying diverse information andcurated a dataset named DiverseSumm. The dataset includes 245 news stories,with each story comprising 10 news articles and paired with a human-validatedreference. Moreover, we conducted a comprehensive analysis to pinpoint theposition and verbosity biases when utilizing Large Language Model (LLM)-basedmetrics for evaluating the coverage and faithfulness of the summaries, as wellas their correlation with human assessments. We applied our findings to studyhow LLMs summarize multiple news articles by analyzing which type of diverseinformation LLMs are capable of identifying. Our analyses suggest that despitethe extraordinary capabilities of LLMs in single-document summarization, theproposed task remains a complex challenge for them mainly due to their limitedcoverage, with GPT-4 only able to cover less than 40% of the diverseinformation on average.

Embrace Divergence for Richer Insights: A Multi-document Summarization  Benchmark and a Case Study on Summarizing Diverse Information from News  Articles