Research Article
Citation Based Collaborative Summarization of Scientific Publications by a New Sentence Similarity Measure
@INPROCEEDINGS{10.1007/978-3-030-00916-8_62, author={Chengzhe Yuan and Dingding Li and Jia Zhu and Yong Tang and Shahbaz Wasti and Chaobo He and Hai Liu and Ronghua Lin}, title={Citation Based Collaborative Summarization of Scientific Publications by a New Sentence Similarity Measure}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings}, proceedings_a={COLLABORATECOM}, year={2018}, month={10}, keywords={Collaborative summarization Sentence similarity measure Citation context}, doi={10.1007/978-3-030-00916-8_62} }- Chengzhe Yuan
Dingding Li
Jia Zhu
Yong Tang
Shahbaz Wasti
Chaobo He
Hai Liu
Ronghua Lin
Year: 2018
Citation Based Collaborative Summarization of Scientific Publications by a New Sentence Similarity Measure
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-00916-8_62
Abstract
Next-generation network offers unrestricted access for researchers to all kinds of scientific publications, collaborative summarization systems are now being contemplated as a service that can help researchers gain information when they read scientific articles. One way to develop a collaborative summarization system is to measure semantic similarity between sentences to improve its quality. In this paper, we introduce a new sentence similarity measure for summarizing scientific articles with citation context. Our work is based on recent work in document distance metric called the word mover’s distance (WMD). Compared to traditional similarity measures, WMD based sentence similarity measure has better performance by capturing the semantic relation between two sentences. Experiments on 2016 version of ACL Anthology Reference Corpus show that our approach outperforms several other baselines by ROUGE metrics.
