Toward Extractive Summarization Of Online Forum Discussions Via Hierarchical Attention Networks
Abstract
Forum threads are lengthy and rich in content. Concise thread summaries will benefit both newcomers seeking information and those who participate in the discussion. Few studies, however, have examined the task of forum thread summarization. In this work we make the first attempt to adapt the hierarchical attention networks for thread summarization. The model draws on the recent development of neural attention mechanisms to build sentence and thread representations and use them for summarization. Our results indicate that the proposed approach can outperform a range of competitive baselines. Further, a redundancy removal step is crucial for achieving outstanding results.
Publication Date
1-1-2017
Publication Title
FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference
Number of Pages
288-292
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
85029537926 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85029537926
STARS Citation
Tarnpradab, Sansiri; Liu, Fei; and Hua, Kien A., "Toward Extractive Summarization Of Online Forum Discussions Via Hierarchical Attention Networks" (2017). Scopus Export 2015-2019. 7127.
https://stars.library.ucf.edu/scopus2015/7127