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        <title>Multiset Dual Summarization for Incongruent News Article Detection</title>
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    <abstract>The prevalence of deceptive and incongruent news headlines has highlighted their substantial role in the propagation of fake news, exacerbating the spread of both misinformation and disinformation. Existing studies on incongruity detection primarily concentrate on estimating the similarity between the encoded representation of headlines and the encoded representation or summary representative vector of the news body. In the process of obtaining the encoded representation of the news body, researchers often consider either sequential encoding or hierarchical encoding of the news body or to acquire a summary representative vector of the news body, they explore techniques like summarization or dual summarization methods. Nevertheless, when it comes to detecting partially incongruent news, dual summarization-based methods tend to outperform hierarchical encoding-based methods. On the other hand, for datasets focused on detecting fake news, where the hierarchical structure within a news article plays a crucial role, hierarchical encoding-based methods tend to perform better than summarization-based methods. Recognizing this contradictory performance of hierarchical encoding-based and summarizationbased methods across datasets with different characteristics, we introduced a novel approach called Multiset Dual Summarization (MDS). MDS combines the strengths of both hierarchical encoding and dual summarization methods to leverage their respective advantages. We conducted experiments on datasets with diverse characteristics, and our findings demonstrate that our proposed model outperforms established state-of-the-art baseline models.</abstract>
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%0 Conference Proceedings
%T Multiset Dual Summarization for Incongruent News Article Detection
%A Sujit, Kumar
%A Rohan, Jaiswal
%A Mohit Ram, Sharma
%A Sanasam Ranbir, Singh
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F sujit-etal-2023-multiset
%X The prevalence of deceptive and incongruent news headlines has highlighted their substantial role in the propagation of fake news, exacerbating the spread of both misinformation and disinformation. Existing studies on incongruity detection primarily concentrate on estimating the similarity between the encoded representation of headlines and the encoded representation or summary representative vector of the news body. In the process of obtaining the encoded representation of the news body, researchers often consider either sequential encoding or hierarchical encoding of the news body or to acquire a summary representative vector of the news body, they explore techniques like summarization or dual summarization methods. Nevertheless, when it comes to detecting partially incongruent news, dual summarization-based methods tend to outperform hierarchical encoding-based methods. On the other hand, for datasets focused on detecting fake news, where the hierarchical structure within a news article plays a crucial role, hierarchical encoding-based methods tend to perform better than summarization-based methods. Recognizing this contradictory performance of hierarchical encoding-based and summarizationbased methods across datasets with different characteristics, we introduced a novel approach called Multiset Dual Summarization (MDS). MDS combines the strengths of both hierarchical encoding and dual summarization methods to leverage their respective advantages. We conducted experiments on datasets with diverse characteristics, and our findings demonstrate that our proposed model outperforms established state-of-the-art baseline models.
%U https://aclanthology.org/2023.icon-1.80
%P 779-790

Markdown (Informal)

[Multiset Dual Summarization for Incongruent News Article Detection](https://aclanthology.org/2023.icon-1.80) (Sujit et al., ICON 2023)

• Multiset Dual Summarization for Incongruent News Article Detection (Sujit et al., ICON 2023)

ACL

• Kumar Sujit, Jaiswal Rohan, Sharma Mohit Ram, and Singh Sanasam Ranbir. 2023. Multiset Dual Summarization for Incongruent News Article Detection. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 779–790, Goa University, Goa, India. NLP Association of India (NLPAI).