@inproceedings{shah-etal-2020-hindi,
title = "{H}indi History Note Generation with Unsupervised Extractive Summarization",
author = "Shah, Aayush and
Ramasubbu, Dhineshkumar and
Mathew, Dhruv and
Gadoya, Meet Chetan",
editor = "Shmueli, Boaz and
Huang, Yin Jou",
booktitle = "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 = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-srw.7",
pages = "44--49",
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.",
}
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%0 Conference Proceedings
%T Hindi History Note Generation with Unsupervised Extractive Summarization
%A Shah, Aayush
%A Ramasubbu, Dhineshkumar
%A Mathew, Dhruv
%A Gadoya, Meet Chetan
%Y Shmueli, Boaz
%Y Huang, Yin Jou
%S 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
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F shah-etal-2020-hindi
%X 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.
%U https://aclanthology.org/2020.aacl-srw.7
%P 44-49
Markdown (Informal)
[Hindi History Note Generation with Unsupervised Extractive Summarization](https://aclanthology.org/2020.aacl-srw.7) (Shah et al., AACL 2020)
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.