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Neural Computing and Applications

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23.07.2020 | Original Article

SHEG: summarization and headline generation of news articles using deep learning

verfasst von: Rajeev Kumar Singh, Sonia Khetarpaul, Rohan Gorantla, Sai Giridhar Allada

Erschienen in: Neural Computing and Applications | Ausgabe 8/2021

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Abstract

The human attention span is continuously decreasing, and the amount of time a person wants to spend on reading is declining at an alarming rate. Therefore, it is imperative to provide a quick glance of important news by generating a concise summary of the prominent news article, along with the most intuitive headline in line with the summary. When humans produce summaries of documents, they not only extract phrases and concatenate them but also produce new grammatical phrases or sentences that coincide with each other and capture the most significant information of the original article. Humans have an incredible ability to create abstractions; however, automatic summarization is a challenging problem. This paper aims to develop an end-to-end methodology that can generate brief summaries and crisp headlines that can capture the attention of readers and convey a significant amount of relevant information. In this paper, we propose a novel methodology known as SHEG, which is designed as a hybrid model. It works by integrating both extractive and abstractive mechanisms using a pipelined approach to produce a concise summary, which is then used for headline generation. Experiments were performed on publicly available datasets, viz. CNN/Daily Mail, Gigaword, and NEWSROOM. The results obtained validate our approach and demonstrate that the proposed SHEG model is effectively producing a concise summary as well as a captivating and fitting headline.

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Titel: SHEG: summarization and headline generation of news articles using deep learning
verfasst von: Rajeev Kumar Singh
Sonia Khetarpaul
Rohan Gorantla
Sai Giridhar Allada
Publikationsdatum: 23.07.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05188-9

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