Sentiment Lossless Summarization

Li, Xiaodong; Wu, Pangjing; Zou, Chenxin; Xie, Haoran; Wang, Fu Lee

doi:10.1016/j.knosys.2021.107170

Cited by 9 publications

(2 citation statements)

References 31 publications

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“…Similarly, Lierde and Chow [55] presented a query-focused ATS system based on hypergraphs, where sentences were mentioned as graph nodes and hyperedges combined the nodes of similar topics. Li et al [56] argued that summarization systems either fail to contain the sentiments in the summary, or give wrong sentiments from the original document sets altogether. They used graph theory for summarization as well as to maintain the sentiment vector for sentences to keep the sentiments of the documents in the summary intact.…”

Section: State-of-the-art Multi-document Summarization Techniquesmentioning

confidence: 99%

Grapharizer: A Graph-Based Technique for Extractive Multi-Document Summarization

et al. 2023

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In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%.

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Section: State-of-the-art Multi-document Summarization Techniquesmentioning

confidence: 99%

Grapharizer: A Graph-Based Technique for Extractive Multi-Document Summarization

et al. 2023

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“…Abdi et al (2019) presented a two-phase sentiment-oriented summarization approach that integrates sentiment facts, statistical analysis and linguistic methods to enhance sentence ranking. Li et al (2021) introduced a graph-based extractive summarization approach that integrates a sentiment compensation mechanism. Sentiment compensation facilitates sentiment consistency between summaries and source documents during summarization.…”

Section: Related Workmentioning

confidence: 99%

Advanced intelligent health advice with informative summaries to facilitate treatment decision-making

Liu

Chen

2023

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Purpose Whether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health professionals becomes an important issue. This paper aims to develop a novel deep learning-based summarization approach for obtaining the most informative summaries from online patient reviews accurately and effectively. Design/methodology/approach This paper proposes a framework to generate summaries that integrates a domain-specific pre-trained embedding model and a deep neural extractive summary approach by considering content features, text sentiment, review influence and readability features. Representative health-related summaries were identified, and user judgements were analysed. Findings Experimental results on the three real-world health forum data sets indicate that awarding sentences without incorporating all the adopted features leads to declining summarization performance. The proposed summarizer significantly outperformed the comparison baseline. User judgement through the questionnaire provides realistic and concrete evidence of crucial features that remarkably influence patient forum review summaries. Originality/value This study contributes to health analytics and management literature by exploring users’ expressions and opinions through the health deep learning summarization model. The research also developed an innovative mindset to design summarization weighting methods from user-created content on health topics.

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