Text Summarization Using Combination of Sequence-To-Sequence Model with Attention Approach

Title

Text Summarization Using Combination of Sequence-To-Sequence Model with Attention Approach

Creator

Bhandarkar P.; Thomas K.T.

Description

In daily life, we come across tons and tons of information which can be related to news articles or any kind of social media posts or customer reviews related to product. It is difficult to read all the content due to time constraint. Being able to develop the software that can identify and automatically extract the important information. There are two types of summarization methods. Extractive text summarization is the method where it picks the important content from the source text and gives same in the form of short summary, and on the other hand, abstractive summarization is the technique where it gets the context of the source text, and based on that context, it regenerates small and crisps summary. In this paper, we use the concept of neural network with attention layer to deal with abstractive text summarization that generates short summary of a long piece of text using review dataset. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes on Data Engineering and Communications Technologies, Vol-141, pp. 283-293.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Attention layer; Decoderencoder; Long-term short memory; Recurrent neural network; Text summarization

Coverage

Bhandarkar P., Department of Data Science, CHRIST University, Bangalore, India; Thomas K.T., Department of Data Science, CHRIST University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23674512

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-19-3035-5_22

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140240291&doi=10.1007%2f978-981-19-3035-5_22&partnerID=40&md5=24f895270b131fea51a6e9d7b397f3ad