Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations

Ryuji Kano, Yasuhide Miura, Motoki Taniguchi, Yan-Ying Chen, Francine Chen, Tomoko Ohkuma


Abstract
We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.
Anthology ID:
D18-1144
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1139–1145
Language:
URL:
https://aclanthology.org/D18-1144
DOI:
10.18653/v1/D18-1144
Bibkey:
Cite (ACL):
Ryuji Kano, Yasuhide Miura, Motoki Taniguchi, Yan-Ying Chen, Francine Chen, and Tomoko Ohkuma. 2018. Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1139–1145, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations (Kano et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1144.pdf