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Fahd A. Ghanem, M. C. Padma and Ramez Alkhatib
The rapid expansion of social media platforms has resulted in an unprecedented surge of short text content being generated on a daily basis. Extracting valuable insights and patterns from this vast volume of textual data necessitates specialized techniqu...
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Jai Prakash Verma, Shir Bhargav, Madhuri Bhavsar, Pronaya Bhattacharya, Ali Bostani, Subrata Chowdhury, Julian Webber and Abolfazl Mehbodniya
The recent advancements in big data and natural language processing (NLP) have necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous textual data. Text summarization (TS) acts as an essential pillar within recommendat...
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K. Manju, S. David Peter and Sumam Mary Idicula
Automatic extractive text summarization retrieves a subset of data that represents most notable sentences in the entire document. In the era of digital explosion, which is mostly unstructured textual data, there is a demand for users to understand the hu...
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Biqing Zeng, Ruyang Xu, Heng Yang, Zibang Gan and Wu Zhou
Under the constraint of memory capacity of the neural network and the document length, it is difficult to generate summaries with adequate salient information. In this work, the self-matching mechanism is incorporated into the extractive summarization sy...
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Qicai Wang, Peiyu Liu, Zhenfang Zhu, Hongxia Yin, Qiuyue Zhang and Lindong Zhang
As a core task of natural language processing and information retrieval, automatic text summarization is widely applied in many fields. There are two existing methods for text summarization task at present: abstractive and extractive. On this basis we pr...
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Marios Koniaris, Dimitris Galanis, Eugenia Giannini and Panayiotis Tsanakas
The increasing amount of legal information available online is overwhelming for both citizens and legal professionals, making it difficult and time-consuming to find relevant information and keep up with the latest legal developments. Automatic text summ...
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Kang Yang, Kamal Al-Sabahi, Yanmin Xiang and Zuping Zhang
Extractive summarization aims to produce a concise version of a document by extracting information-rich sentences from the original texts. The graph-based model is an effective and efficient approach to rank sentences since it is simple and easy to use. ...
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Shailza Jolly, Pepa Atanasova and Isabelle Augenstein
Fact-checking systems have become important tools to verify fake and misguiding news. These systems become more trustworthy when human-readable explanations accompany the veracity labels. However, manual collection of these explanations is expensive and ...
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Yong Zhang, Dan Li, Yuheng Wang, Yang Fang and Weidong Xiao
Abstract text summarization aims to offer a highly condensed and valuable information that expresses the main ideas of the text. Most previous researches focus on extractive models. In this work, we put forward a new generative model based on convolution...
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Afrida Helen
Pág. 22 - 34
Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses ...
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Jean Louis Ebongue Kedieng Fendji, Désiré Manuel Taira, Marcellin Atemkeng and Adam Musa Ali
Text summarization remains a challenging task in the natural language processing field despite the plethora of applications in enterprises and daily life. One of the common use cases is the summarization of web pages which has the potential to provide an...
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Andrea Chaves, Cyrille Kesiku and Begonya Garcia-Zapirain
In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential...
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