Enhancing Incremental Summarization with Structured Representations

EunJeong Hwang, Yichao Zhou, James Bradley Wendt, Beliz Gunel, Nguyen Vo, Jing Xie, Sandeep Tata


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.
Anthology ID:
2024.findings-emnlp.220
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3830–3842
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.220/
DOI:
10.18653/v1/2024.findings-emnlp.220
Bibkey:
Cite (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.
Cite (Informal):
Enhancing Incremental Summarization with Structured Representations (Hwang et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-emnlp.220.pdf