Hindi History Note Generation with Unsupervised Extractive Summarization

Aayush Shah, Dhineshkumar Ramasubbu, Dhruv Mathew, Meet Chetan Gadoya


Abstract
In this work, the task of extractive single document summarization applied to an education setting to generate summaries of chapters from grade 10 Hindi history textbooks is undertaken. Unsupervised approaches to extract summaries are employed and evaluated. TextRank, LexRank, Luhn and KLSum are used to extract summaries. When evaluated intrinsically, Luhn and TextRank summaries have the highest ROUGE scores. When evaluated extrinsically, the effective measure of a summary in answering exam questions, TextRank summaries performs the best.
Anthology ID:
2020.aacl-srw.7
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Boaz Shmueli, Yin Jou Huang
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
44–49
Language:
URL:
https://aclanthology.org/2020.aacl-srw.7
DOI:
Bibkey:
Cite (ACL):
Aayush Shah, Dhineshkumar Ramasubbu, Dhruv Mathew, and Meet Chetan Gadoya. 2020. Hindi History Note Generation with Unsupervised Extractive Summarization. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 44–49, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Hindi History Note Generation with Unsupervised Extractive Summarization (Shah et al., AACL 2020)
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PDF:
https://aclanthology.org/2020.aacl-srw.7.pdf