@inproceedings{hwang-etal-2024-enhancing,
title = "Enhancing Incremental Summarization with Structured Representations",
author = "Hwang, EunJeong and
Zhou, Yichao and
Wendt, James Bradley and
Gunel, Beliz and
Vo, Nguyen and
Xie, Jing and
Tata, Sandeep",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.220/",
doi = "10.18653/v1/2024.findings-emnlp.220",
pages = "3830--3842",
abstract = "Large language models (LLMs) often struggle with processing extensive input contexts, which can lead to redundant, inaccurate, or incoherent summaries. Recent methods have used unstructured memory to incrementally process these contexts, but they still suffer from information overload due to the volume of unstructured data handled. In our study, we introduce structured knowledge representations (GU{\_}json), which significantly improve summarization performance by 40{\%} and 14{\%} across two public datasets. Most notably, we propose the Chain-of-Key strategy (CoK{\_}json) that dynamically updates or augments these representations with new information, rather than recreating the structured memory for each new source. This method further enhances performance by 7{\%} and 4{\%} on the datasets."
}
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<abstract>Large language models (LLMs) often struggle with processing extensive input contexts, which can lead to redundant, inaccurate, or incoherent summaries. Recent methods have used unstructured memory to incrementally process these contexts, but they still suffer from information overload due to the volume of unstructured data handled. In our study, we introduce structured knowledge representations (GU_json), which significantly improve summarization performance by 40% and 14% across two public datasets. Most notably, we propose the Chain-of-Key strategy (CoK_json) that dynamically updates or augments these representations with new information, rather than recreating the structured memory for each new source. This method further enhances performance by 7% and 4% on the datasets.</abstract>
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%0 Conference Proceedings
%T Enhancing Incremental Summarization with Structured Representations
%A Hwang, EunJeong
%A Zhou, Yichao
%A Wendt, James Bradley
%A Gunel, Beliz
%A Vo, Nguyen
%A Xie, Jing
%A Tata, Sandeep
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F hwang-etal-2024-enhancing
%X Large language models (LLMs) often struggle with processing extensive input contexts, which can lead to redundant, inaccurate, or incoherent summaries. Recent methods have used unstructured memory to incrementally process these contexts, but they still suffer from information overload due to the volume of unstructured data handled. In our study, we introduce structured knowledge representations (GU_json), which significantly improve summarization performance by 40% and 14% across two public datasets. Most notably, we propose the Chain-of-Key strategy (CoK_json) that dynamically updates or augments these representations with new information, rather than recreating the structured memory for each new source. This method further enhances performance by 7% and 4% on the datasets.
%R 10.18653/v1/2024.findings-emnlp.220
%U https://aclanthology.org/2024.findings-emnlp.220/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.220
%P 3830-3842
Markdown (Informal)
[Enhancing Incremental Summarization with Structured Representations](https://aclanthology.org/2024.findings-emnlp.220/) (Hwang et al., Findings 2024)
ACL
- EunJeong Hwang, Yichao Zhou, James Bradley Wendt, Beliz Gunel, Nguyen Vo, Jing Xie, and Sandeep Tata. 2024. Enhancing Incremental Summarization with Structured Representations. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3830–3842, Miami, Florida, USA. Association for Computational Linguistics.