An abstractive text summarization using deep learning in Assamese

被引：1
作者：
Goutom P.J. [1 ]
Baruah N. [1 ]
Sonowal P. [1 ]
机构：
[1] Dibrugarh University, Assam, Dibrugarh
来源：
International Journal of Information Technology | 2023年 / 15卷 / 5期
关键词：
Abstractive; Attention; NLP; Seq2Seq; Text summarization;
D O I：
10.1007/s41870-023-01279-7
中图分类号：
学科分类号：
摘要：
Abstractive text summarization with long short-term memory (LSTM) is a prominent strategy in natural language processing that tries to construct a compact and coherent summary of a given text by learning the semantic representation of the input text. In this study, we provide a seq2seq-based LSTM network model with attention to the encoder–decoder to construct a short sequence of words containing coherent and human-like created summaries, including crucial information from the original text. We obtained a dataset from Asomiya Pratidin, an Assamese online news website, including around 10,000 Assamese text articles and related human-written summaries. Our primary objective is to create an abstractive summarizer in Assamese while minimizing train loss. We reduced train loss to 0.3032 and produced fluent summaries during our study. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
引用
收藏
页码：2365 / 2372
页数：7
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