Unsupervised Abstractive Summarization of Bengali Text Documents

Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, Taufiqul Jannat


Abstract
Abstractive summarization systems generally rely on large collections of document-summary pairs. However, the performance of abstractive systems remains a challenge due to the unavailability of the parallel data for low-resource languages like Bengali. To overcome this problem, we propose a graph-based unsupervised abstractive summarization system in the single-document setting for Bengali text documents, which requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts. We also provide a human-annotated dataset with document-summary pairs to evaluate our abstractive model and to support the comparison of future abstractive summarization systems of the Bengali Language. We conduct experiments on this dataset and compare our system with several well-established unsupervised extractive summarization systems. Our unsupervised abstractive summarization model outperforms the baselines without being exposed to any human-annotated reference summaries.
Anthology ID:
2021.eacl-main.224
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2612–2619
Language:
URL:
https://aclanthology.org/2021.eacl-main.224
DOI:
10.18653/v1/2021.eacl-main.224
Bibkey:
Cite (ACL):
Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, and Taufiqul Jannat. 2021. Unsupervised Abstractive Summarization of Bengali Text Documents. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2612–2619, Online. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Abstractive Summarization of Bengali Text Documents (Chowdhury et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.224.pdf
Code
 tafseer-nayeem/BengaliSummarization