Detecting Incongruent News Articles Using Multi-head Attention Dual Summarization (2022)

Abstract: With the increasing use of influencing incongruent news headlines for spreading fake news, detecting incongruent news articles has become an important research challenge. Most of the earlier studies on incongruity detection focus on estimating the similarity between the headline and the encoding of the body or its summary. However, most of these methods fail to handle incongruent news articles created with embedded noise. Motivated by the above issue, this paper proposes a Multi-head Attention Dual Summary (MADS) based method which generates two types of summaries that capture the congruent and incongruent parts in the body separately. From various experimental setups over three publicly available datasets, it is evident that the proposed model outperforms the state-of-the-art baseline counterparts.

## introduction news headlines greatly influence opinion of the readers @xcite and play a significant role in making a new viral on any social media @xcite ) @xcite . a deceitful and incongruent news article can negatively affect readers, such as false beliefs and wrong opinions 12 @xcite @xcite . if a news headline misrepresents the content of its body then such headline and body pair is called incongruent news article @xcite . in recent times, usage of deceptive and incongruent news headlines as an effective means to spread disinformation over digital platforms is evident @xcite (effron and raj, 2020) 34 . consequently, detecting deceitful and incongruent news articles @xcite (horner et al., 1 impact of misleading headline in health 2 misleading headlines effect on economy news 3 examples of misleading headline fake news 4 misleading headline fake news over who 2021) @xcite is becoming an important research problem to counter the spread of misinformation over digital media. an incongruent news article may be constituted in various forms (i) the headline makes unrelated or opposite claims to its body, (ii) both headline and body refer to a common topic or event, but the contents are not related, (iii) both headline and body report a genuine event/incident, but the dates or name entities are manipulated, (iv) methods are earlier studies on incongruent news detection mainly focuses on estimating dissimilarity between headline and body using methods such as bag-of-words based features @xcite , @xcite , @xcite , sequential encoding of headline and body @xcite , @xcite , and hierarchical encoding of the news article @xcite , @xcite , @xcite . as reported in @xcite , the above similaritybased methods generally fail to detect incongruent news for the news article body with larger paragraphs and sentences. to address these problems, recent studies @xcite , @xcite , @xcite propose summarization-based approaches. as the summarization in these studies are biased towards the dominant content of the body, such summarization may fail to capture the embedding noise present in partially incongruent news articles. motivated by this, this paper proposes a multi-head attention dual summarization m ads based summarization method which is capable of handling partially incongruent news by summarizing both the congruent and incongruent part of the article body. the proposed method divides the body of the news article into two sets -positive: highly congruent sentences with headline and negative: highly incongruent sentences with headline. further, for each set, different forms of representation are cap-tured using multi-head attention and convolution. from various experiments over three publicly available benchmark datasets, it is observed that the proposed method outperforms the existing state-ofthe-art baseline counterparts, including the dataset with partially incongruent news article. ## related work though both the clickbait and incongruent news article detection relate to news headline, as discussed in @xcite , @xcite , clickbait headline can be detected based on the headline only, whereas incongruent news article is defined by the relation between the headline and the news article body @xcite . clickbait attempts to attract the reader's attention, but incongruent news articles do not force readers to click some link and follow up @xcite . our paper focuses on incongruent detection. studies on incongruent news article detection can be broadly categorized into similarity-based and summarizationbased approaches. initial studies @xcite , @xcite , @xcite , @xcite used bag-of-word based features and sequential encoding to discover similarity between headline and body to detect incongruity. further studies under similarity-based approaches exploit attention between headline and body @xcite @xcite for incongruent news article detection. studies @xcite , @xcite utilize hierarchical structure of news article to highlight important sentences in body with respect to claim of headline. however, the similaritybased approach performs average when the news article body is significantly large (high number of words and sentences) compared to the headline's length @xcite , @xcite . also, similarity-based methods fail to detect partially incongruent news articles. to overcome the limitations of the similarity-based approach, studies @xcite , @xcite ) make use of the summarization technique to summarize news articles body to pieces of text. subsequently, text matching methods are applied between the summary of the news article body and the headline. studies @xcite exploit graph summarization to detect fake news articles. study @xcite ) make use of part of speech tag patterns(pos) based attention to take cognizance of numerical value of headlines and body for incongruent news article detection. considering the importance of bidirectional context in documents, study @xcite propose robert-based models for fake news detections. a recent study @xcite utilizes news subtitle, image caption, headline and body along with attention between headline and body to detect incongruent headline. as the summarization in these studies are biased towards the dominant content of the body, such summarization may fail to capture the embedding noise present in partially incongruent news articles. hence, we need an incongruent news article detection-specific summarization technique, which should focus more on the incongruent part of the news article while generating a summary of news article body. considering such limitations of summarization-based approach for incongruent news detection, this paper proposes a multi-head attention dual summarization model m ads which divide the body into two sets : positive set and negative set. if the similarity score of a sentence with the headline is high, then it is placed in a positive set and otherwise placed in a negative set. then a summary of both sets is obtained separately and matched with the headline for incongruent news article detection. ## proposed models given a news article @xmath0, b with a pair of its headlines h and its body b, m ads divides the sentences in the body b into positive p and negative n sets based on the matching scores between the sentence s i and the headline h. the main motivation behind splitting body sentences into positive p and negative n sets is that if a news article is partially incongruent, then sentences congruent with the headline will be in positive set p and sentences incongruent with a headline will be in negative set n . similarly, in the case of a full congruent news article, most of the sentences of the body should be in p set, and only few sentences will be in n set. however, if a news article is fully incongruent, then all the sentences in the body should be incongruent with the headline; hence it should be in n except one or few sentences in p. next, summary of p and n are obtained separately to match with if m @xmath1, the sentence is placed in the positive set otherwise, it is placed in the negative set. then we generate summary of these positive and negative set using multi-head attention and convolution. thereafter, text matching features between headline and representative summary generated from multi-head attention and convolution is obtained and passed to the two fully connected layers for the classification. ## conclusion and future work this paper proposed a multi-head attention dual summarization model, m ads, for detecting incongruent news articles of different characteristics. m ads extract two different types of summary, viz. multi-head attention and convolution summary over positive and negative set separately. subsequently, summaries obtained are matched with headline for incongruent news article detection. it is conclusive from our experimental results that our model m ads is superior in performance to other baseline models across three benchmark datasets. in addition, we conclude that m ads is capable of detecting both incongruent and partially incongruent news articles. this work can be extended to multiple directions in the future. one such direction could be generating topic-aware summarization where the topic of the headline is identified, specific to which the article body is summarized. generating knowledge-based summarization is another avenue where the summarization is backed by some knowledge bases like wikipedia etc.