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Exploring Text Summarization for Fake News Detection
Jie Bian, Seungeon Lee, Karandeep Singh, Meeyoung Cha
http://doi.org/10.5626/JOK.2022.49.11.926
Fake news detection models need to gather and ingest massive information from heterogeneous sources rapidly for solid verification. This paper demonstrates the feasibility of applying text summarization, to uncover useful information or evidence for fake news detection. Two popular deep learning-based summarization techniques, extractive and abstractive, were used to generate condensed textual information from lengthy news content. Experiments on popular rumor debunking datasets show that two lines of summarized text can extract critical information, while improving the classification performance and substantially reducing inference time. Text summarization can also bring explainability by providing evidence from three levels: words, sentences, and documents.
News Stream Summarization for an Event based on Timeline
Ian Jung, Su Jeong Choi, Seyoung Park
http://doi.org/10.5626/JOK.2019.46.11.1140
This paper explores the summarization task in news stream, as it is continuously produced and has sequential characteristic. Timeline based summarization is widely adopted in news stream summarization because timeline can represent events sequentially. However, previous work relies on the time of collection of news article, thus they cannot consider for dates other than out of the collected period. In addition, previous work lacked consideration of conciseness, informativeness, and coherence. To address these problems, we propose a news stream summarization model with an expanded timeline. The model takes into consideration the expanded timeline by using time points that are referenced in given news articles and selects sentences that are concise, informative and consistent with neighboring time points. First, we constitute expanded timeline by selecting dates which are from all identified time points in the news articles. Then, we extract sentences as summary with consideration of informativeness based on keyword for each time points, and on coherence between two consecutive time points, and on continuity of named entities except for long sentence in the articles. Experimental results show that the proposed model generated higher quality summaries compared to previous work.
Document Summarization Using TextRank Based on Sentence Embedding
Seok-won Jeong, Jintae Kim, Harksoo Kim
http://doi.org/10.5626/JOK.2019.46.3.285
Document summarization is creating a short version document that maintains the main content of original document. An extractive summarization has been actively studied by the reason of it guarantees the basic level of grammar and high level of accuracy by copying a large amount of text from the original document. It is difficult to consider the meaning of sentences because the TextRank, which is a typical extractive summarization method, calculates an edge of graph through the frequency of words. In a bid to solve these drawbacks, we propose a new TextRank using sentence embedding. Through experiments, we confirmed that the proposed method can consider the meaning of the sentence better than the existing method.
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