Unsupervised Approach to Multilingual User Comments Summarization

Aleš Žagar, Marko Robnik-Šikonja


Abstract
User commenting is a valuable feature of many news outlets, enabling them a contact with readers and enabling readers to express their opinion, provide different viewpoints, and even complementary information. Yet, large volumes of user comments are hard to filter, let alone read and extract relevant information. The research on the summarization of user comments is still in its infancy, and human-created summarization datasets are scarce, especially for less-resourced languages. To address this issue, we propose an unsupervised approach to user comments summarization, which uses a modern multilingual representation of sentences together with standard extractive summarization techniques. Our comparison of different sentence representation approaches coupled with different summarization approaches shows that the most successful combinations are the same in news and comment summarization. The empirical results and presented visualisation show usefulness of the proposed methodology for several languages.
Anthology ID:
2021.hackashop-1.13
Volume:
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation
Month:
April
Year:
2021
Address:
Online
Venues:
EACL | Hackashop
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
89–98
Language:
URL:
https://aclanthology.org/2021.hackashop-1.13
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.hackashop-1.13.pdf
Code
 azagsam/xl-user-comments