Abstract
With the rapid growth of scientific papers, understanding the changes and trends in a research area is rather time-consuming. The first challenge is to find related and comparable articles
for the research. Comparative citations compare co-cited papers in a citation sentence and can serve as good guidance for researchers to track a research area. We thus go through
comparative citations to find comparable objects and build a comparative scientific summarization corpus (CSSC). And then, we propose the comparative graph-based summarization
(CGSUM) method to create comparative summaries using citations as guidance. The comparative graph is constructed using sentences as nodes and three different relationships of sentences
as edges. The relationship that sentences occur in the same paper is used to calculate the salience of sentences, the relationship that sentences occur in two different papers is used to calculate the difference between sentences, and the relationship that sentences are related to citations is used to calculate the commonality of
sentences. Experiments show that CGSUM outperforms
comparative baselines on CSSC and performs well on DUC2006 and DUC2007.
for the research. Comparative citations compare co-cited papers in a citation sentence and can serve as good guidance for researchers to track a research area. We thus go through
comparative citations to find comparable objects and build a comparative scientific summarization corpus (CSSC). And then, we propose the comparative graph-based summarization
(CGSUM) method to create comparative summaries using citations as guidance. The comparative graph is constructed using sentences as nodes and three different relationships of sentences
as edges. The relationship that sentences occur in the same paper is used to calculate the salience of sentences, the relationship that sentences occur in two different papers is used to calculate the difference between sentences, and the relationship that sentences are related to citations is used to calculate the commonality of
sentences. Experiments show that CGSUM outperforms
comparative baselines on CSSC and performs well on DUC2006 and DUC2007.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 29th International Conference on Computational Linguistics (COLING'22) |
| Publisher | ACL (Association for Computational Linguistics) |
| Pages | 5978–5988 |
| Number of pages | 11 |
| Volume | 29 |
| Edition | 1 |
| Publication status | Published - Oct 2022 |
| Event | 29th International Conference on Computational Linguistics - Gyeongju, Korea, Republic of Duration: 12 Oct 2022 → 17 Oct 2022 Conference number: 29 https://coling2022.org/ |
Publication series
| Name | Proceedings - International Conference on Computational Linguistics, COLING |
|---|---|
| ISSN (Print) | 2951-2093 |
Conference
| Conference | 29th International Conference on Computational Linguistics |
|---|---|
| Abbreviated title | coling 2022 |
| Country/Territory | Korea, Republic of |
| City | Gyeongju |
| Period | 12/10/22 → 17/10/22 |
| Internet address |
Bibliographical note
Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. (CC BY)Funding
| Funders | Funder number |
|---|---|
| National Natural Science Foundation of China | 61806101 |