Hindi Document Extractive Summarization: Neural Method on A New Data Set

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI:10.1109/CINE56307.2022.10037327

Kunal Tawatia, Nishant Jain, Suman Kundu

{"title":"Hindi Document Extractive Summarization: Neural Method on A New Data Set","authors":"Kunal Tawatia, Nishant Jain, Suman Kundu","doi":"10.1109/CINE56307.2022.10037327","DOIUrl":null,"url":null,"abstract":"Extractive summarization is one of the vital tasks in text analysis and natural language processing. Although Hindi is one of the world's highly speaking languages and produces thousands of online documents daily, most existing text summarization works focus on the English language. A Neural network-based summarizer is popular for abstractive summarization but has not been explored for extractive one except in a few recent studies. The present work uses a neural extractive summarizing model to develop a Hindi language extractive summarizer. The main contribution of the paper is two-fold. First, we generated a new Hindi-based text summarization data set from a popular Hindi news channel AajTak. The code to generate the data set is available at https://tinyurl.com/sonaa-hindi-text. Then we use this data set to train a Neural Extractive Summarization model. The model also learns the word embeddings while learning itself. The ROUGE-2-Fl and ROUGE-1-Fl results on test data show promising output with a score of 20.02 and 39.81, respectively.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINE56307.2022.10037327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

Abstract

Extractive summarization is one of the vital tasks in text analysis and natural language processing. Although Hindi is one of the world's highly speaking languages and produces thousands of online documents daily, most existing text summarization works focus on the English language. A Neural network-based summarizer is popular for abstractive summarization but has not been explored for extractive one except in a few recent studies. The present work uses a neural extractive summarizing model to develop a Hindi language extractive summarizer. The main contribution of the paper is two-fold. First, we generated a new Hindi-based text summarization data set from a popular Hindi news channel AajTak. The code to generate the data set is available at https://tinyurl.com/sonaa-hindi-text. Then we use this data set to train a Neural Extractive Summarization model. The model also learns the word embeddings while learning itself. The ROUGE-2-Fl and ROUGE-1-Fl results on test data show promising output with a score of 20.02 and 39.81, respectively.

查看原文本刊更多论文

印地语文档抽取摘要:基于新数据集的神经方法

摘要文摘是文本分析和自然语言处理中的重要任务之一。尽管印地语是世界上使用频率最高的语言之一，并且每天产生数千个在线文档，但大多数现有的文本摘要工作都集中在英语上。基于神经网络的摘要器在抽象摘要中很受欢迎，但除了最近的一些研究外，尚未对抽取摘要进行探索。本研究使用神经抽取摘要模型来开发印地语抽取摘要器。这篇论文的主要贡献有两个方面。首先，我们从一个流行的印地语新闻频道AajTak生成了一个新的基于印地语的文本摘要数据集。生成数据集的代码可从https://tinyurl.com/sonaa-hindi-text获得。然后我们使用这些数据集来训练一个神经抽取摘要模型。该模型在学习自身的同时也学习单词嵌入。ROUGE-2-Fl和ROUGE-1-Fl在测试数据上的结果显示出良好的输出，分别为20.02和39.81分。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2022 5th International Conference on Computational Intelligence and Networks (CINE)

自引率

0.00%

发文量