Multi-Document Scientific Summarization from a Knowledge Graph-Centric View

Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, Ting Wang


Abstract
Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understanding of paper content and accurate modeling of cross-paper relationships. Knowledge graphs convey compact and interpretable structured information for documents, which makes them ideal for content modeling and relationship modeling. In this paper, we present KGSum, an MDSS model centred on knowledge graphs during both the encoding and decoding process. Specifically, in the encoding process, two graph-based modules are proposed to incorporate knowledge graph information into paper encoding, while in the decoding process, we propose a two-stage decoder by first generating knowledge graph information of summary in the form of descriptive sentences, followed by generating the final summary. Empirical results show that the proposed architecture brings substantial improvements over baselines on the Multi-Xscience dataset.
Anthology ID:
2022.coling-1.543
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6222–6233
Language:
URL:
https://aclanthology.org/2022.coling-1.543
DOI:
Bibkey:
Cite (ACL):
Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, and Ting Wang. 2022. Multi-Document Scientific Summarization from a Knowledge Graph-Centric View. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6222–6233, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View (Wang et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.543.pdf
Code
 muguruzawang/kgsum