@inproceedings{padmakumar-etal-2025-principled,
title = "Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries",
author = "Padmakumar, Vishakh and
Wang, Zichao and
Arbour, David and
Healey, Jennifer",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1445/",
doi = "10.18653/v1/2025.acl-long.1445",
pages = "29884--29899",
ISBN = "979-8-89176-251-0",
abstract = "While large language models (LLMs) are increasingly capable of handling longer contexts, recent work has demonstrated that they exhibit the {\_}{''}lost in the middle''{\_} phenomenon (Liu et al., 2024) of unevenly attending to different parts of the provided context. This hinders their ability to cover diverse source material in multi-document summarization, as noted in the DiverseSumm benchmark (Huang et al., 2024). In this work, we contend that principled content selection is a simple way to increase source coverage on this task. As opposed to prompting an LLM to perform the summarization in a single step, we explicitly divide the task into three steps{---}(1) reducing document collections to atomic key points, (2) using determinantal point processes (DPP) to perform select key points that prioritize diverse content, and (3) rewriting to the final summary. By combining prompting steps, for extraction and rewriting, with principled techniques, for content selection, we consistently improve source coverage on the DiverseSumm benchmark across various LLMs. Finally, we also show that by incorporating relevance to a provided user intent into the DPP kernel, we can generate {\_}personalized{\_} summaries that cover {\_}relevant{\_} source information while retaining coverage."
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<abstract>While large language models (LLMs) are increasingly capable of handling longer contexts, recent work has demonstrated that they exhibit the _”lost in the middle”_ phenomenon (Liu et al., 2024) of unevenly attending to different parts of the provided context. This hinders their ability to cover diverse source material in multi-document summarization, as noted in the DiverseSumm benchmark (Huang et al., 2024). In this work, we contend that principled content selection is a simple way to increase source coverage on this task. As opposed to prompting an LLM to perform the summarization in a single step, we explicitly divide the task into three steps—(1) reducing document collections to atomic key points, (2) using determinantal point processes (DPP) to perform select key points that prioritize diverse content, and (3) rewriting to the final summary. By combining prompting steps, for extraction and rewriting, with principled techniques, for content selection, we consistently improve source coverage on the DiverseSumm benchmark across various LLMs. Finally, we also show that by incorporating relevance to a provided user intent into the DPP kernel, we can generate _personalized_ summaries that cover _relevant_ source information while retaining coverage.</abstract>
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%0 Conference Proceedings
%T Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries
%A Padmakumar, Vishakh
%A Wang, Zichao
%A Arbour, David
%A Healey, Jennifer
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F padmakumar-etal-2025-principled
%X While large language models (LLMs) are increasingly capable of handling longer contexts, recent work has demonstrated that they exhibit the _”lost in the middle”_ phenomenon (Liu et al., 2024) of unevenly attending to different parts of the provided context. This hinders their ability to cover diverse source material in multi-document summarization, as noted in the DiverseSumm benchmark (Huang et al., 2024). In this work, we contend that principled content selection is a simple way to increase source coverage on this task. As opposed to prompting an LLM to perform the summarization in a single step, we explicitly divide the task into three steps—(1) reducing document collections to atomic key points, (2) using determinantal point processes (DPP) to perform select key points that prioritize diverse content, and (3) rewriting to the final summary. By combining prompting steps, for extraction and rewriting, with principled techniques, for content selection, we consistently improve source coverage on the DiverseSumm benchmark across various LLMs. Finally, we also show that by incorporating relevance to a provided user intent into the DPP kernel, we can generate _personalized_ summaries that cover _relevant_ source information while retaining coverage.
%R 10.18653/v1/2025.acl-long.1445
%U https://aclanthology.org/2025.acl-long.1445/
%U https://doi.org/10.18653/v1/2025.acl-long.1445
%P 29884-29899
Markdown (Informal)
[Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries](https://aclanthology.org/2025.acl-long.1445/) (Padmakumar et al., ACL 2025)
ACL