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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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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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Tulu Tilahun Hailu, Junqing Yu and Tessfu Geteye Fantaye
Text summarization is a process of producing a concise version of text (summary) from one or more information sources. If the generated summary preserves meaning of the original text, it will help the users to make fast and effective decision. However, h...
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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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Brian Rizqi Paradisiaca Darnoto, Daniel Siahaan and Diana Purwitasari
Persuasive content in online news contains elements that aim to persuade its readers and may not necessarily include factual information. Since a news article only has some sentences that indicate persuasiveness, it would be quite challenging to differen...
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Andrea Pozzi, Enrico Barbierato and Daniele Toti
In the last decade, the techniques of news aggregation and summarization have been increasingly gaining relevance for providing users on the web with condensed and unbiased information. Indeed, the recent development of successful machine learning algori...
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Abdullah Al Foysal and Ronald Böck
Nowadays, individuals can be overwhelmed by a huge number of documents being present in daily life. Capturing the necessary details is often a challenge. Therefore, it is rather important to summarize documents to obtain the main information quickly. The...
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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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Moreno La Quatra and Luca Cagliero
The emergence of attention-based architectures has led to significant improvements in the performance of neural sequence-to-sequence models for text summarization. Although these models have proved to be effective in summarizing English-written documents...
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Ashokkumar Palanivinayagam, Claude Ziad El-Bayeh and Robertas Dama?evicius
Machine-learning-based text classification is one of the leading research areas and has a wide range of applications, which include spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modelling. Widely...
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Mihai Alexandru Niculescu, Stefan Ruseti and Mihai Dascalu
Significant progress has been achieved in text generation due to recent developments in neural architectures; nevertheless, this task remains challenging, especially for low-resource languages. This study is centered on developing a model for abstractive...
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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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Kuekyeng Kim, Yuna Hur, Gyeongmin Kim and Heuiseok Lim
In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations?usually ...
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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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Xiujuan Xiang, Guangluan Xu, Xingyu Fu, Yang Wei, Li Jin and Lei Wang
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Xiujuan Xiang, Guangluan Xu, Xingyu Fu, Yang Wei, Li Jin and Lei Wang
Current popular abstractive summarization is based on an attentional encoder-decoder framework. Based on the architecture, the decoder generates a summary according to the full text that often results in the decoder being interfered by some irrelevant in...
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Rizki Darmawan,Romi Satria Wahono
Pág. 109 - 114
As the amount of online information increases, systems that can automatically summarize text in a document become increasingly desirable. The main goal of a text summarization is to present the main ideas in a document in less space. In the create text s...
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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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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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Yuanyuan Li, Yuan Huang, Weijian Huang, Junhao Yu and Zheng Huang
An abstractive summarization model based on the joint-attention mechanism and a priori knowledge is proposed to address the problems of the inadequate semantic understanding of text and summaries that do not conform to human language habits in abstractiv...
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