Enhancing Incremental Summarization with Structured Representations

Fuente: arXiv
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Autores principales: Hwang, EunJeong, Zhou, Yichao, Wendt, James Bradley, Gunel, Beliz, Vo, Nguyen, Xie, Jing, Tata, Sandeep
Formato: Preprint
Publicado: 2024
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author Hwang, EunJeong
Zhou, Yichao
Wendt, James Bradley
Gunel, Beliz
Vo, Nguyen
Xie, Jing
Tata, Sandeep
author_facet Hwang, EunJeong
Zhou, Yichao
Wendt, James Bradley
Gunel, Beliz
Vo, Nguyen
Xie, Jing
Tata, Sandeep
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2407_15021
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Enhancing Incremental Summarization with Structured Representations
Hwang, EunJeong
Zhou, Yichao
Wendt, James Bradley
Gunel, Beliz
Vo, Nguyen
Xie, Jing
Tata, Sandeep
Computation and Language
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.
title Enhancing Incremental Summarization with Structured Representations
topic Computation and Language
url https://arxiv.org/abs/2407.15021