Hindi History Note Generation with Unsupervised Extractive Summarization

被引：0

作者：

Shah, Aayush ^{[1
]}

Ramasubbu, Dhineshkumar ^{[1
]}

Mathew, Dhruv ^{[1
]}

Gadoya, Meet Chetan ^{[1
]}

机构：

[1] Univ Southern Calif, Los Angeles, CA 90007 USA

来源：

AACL-IJCNLP 2020: THE 1ST CONFERENCE OF THE ASIA-PACIFIC CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 10TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING: PROCEEDINGS OF THE STUDENT RESEARCH WORKSHOP | 2020年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：44 / 49

页数：6

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