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2021 | OriginalPaper | Chapter

Exploring Summarization to Enhance Headline Stance Detection

Authors : Robiert Sepúlveda-Torres, Marta Vicente, Estela Saquete, Elena Lloret, Manuel Palomar

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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Abstract

The spread of fake news and misinformation is causing serious problems to society, partly due to the fact that more and more people only read headlines or highlights of news assuming that everything is reliable, instead of carefully analysing whether it can contain distorted or false information. Specifically, the headline of a correctly designed news item must correspond to a summary of the main information of that news item. Unfortunately, this is not always happening, since various interests, such as increasing the number of clicks as well as political interests can be behind of the generation of a headlines that does not meet its intended original purpose. This paper analyses the use of automatic news summaries to determine the stance (i.e., position) of a headline with respect to the body of text associated with it. To this end, we propose a two-stage approach that uses summary techniques as input for both classifiers instead of the full text of the news body, thus reducing the amount of information that must be processed while maintaining the important information. The experimentation has been carried out using the Fake News Challenge FNC-1 dataset, leading to a 94.13% accuracy, surpassing the state of the art. It is especially remarkable that the proposed approach, which uses only the relevant information provided by the automatic summaries instead of the full text, is able to classify the different stance categories with very competitive results, so it can be concluded that the use of the automatic extractive summaries has a positive impact for determining the stance of very short information (i.e., headline, sentence) with respect to its whole content.

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previous chapter Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting

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Implementation available at https://github.com/rsepulveda911112/Headline-Stance-Detection.

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Title: Exploring Summarization to Enhance Headline Stance Detection
Authors: Robiert Sepúlveda-Torres
Marta Vicente
Estela Saquete
Elena Lloret
Manuel Palomar
Publisher: Springer International Publishing
Book: Natural Language Processing and Information Systems
Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-80599-9_22

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