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Keyword Extraction and Summarization
2021This chapter focuses on the problems of keyword extraction and text summarization. With regards to keyword extraction, the concept of word centrality is presented and two standard approaches to keyword extraction are introduced. In the case of text summarization, the extractive summarization approach is introduced and discussed.
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Augmenting Neural Sentence Summarization Through Extractive Summarization
2018Neural sequence-to-sequence model has achieved great success in abstractive summarization task. However, due to the limit of input length, most of previous works can only utilize lead sentences as the input to generate the abstractive summarization, which ignores crucial information of the document.
Junnan Zhu +5 more
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Two-stage encoding Extractive Summarization
2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC), 2020Pre-trained language model can express the semantics of word or text span, is widely applied in many NLP tasks, and text summarization is no exception. It is created using fine-tuning or feature-based method on pre-training model. Since Bidirectional Encoder Representations from Transformers (BERT; Devlin et al.
Wenying Guo +3 more
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Abstractive Summarization with the Aid of Extractive Summarization
2018Currently the abstractive method and extractive method are two main approaches for automatic document summarization. To fully integrate the relatedness and advantages of both approaches, we propose in this paper a general framework for abstractive summarization which incorporates extractive summarization as an auxiliary task.
Yangbin Chen +3 more
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Extractive Text Summarization for Wolaita
International Journal of Emerging Trends in Engineering Research, 2022Text summarization is the mechanism of summarizing a huge document comprising vast amount of information which is difficult to overcome and understand its message easily in any written documents for whatever languages without losing its entire message. A short and precise document which conveys intended information for the user in demand is expected in
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Knowledge Distillation on Extractive Summarization
2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 2020Large-scale pre-trained frameworks have shown state-of-the-art performance in several natural language processing tasks. However, the costly training and inference time are great challenges when deploying such models to real-world applications. In this work, we conduct an empirical study of knowledge distillation on an extractive text summarization ...
Ying-Jia Lin +4 more
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Extractive summarization using semigraph (ESSg)
Evolving Systems, 2018Summary is the meaningful concise version of a text document. Generally existing statistical, knowledge based and discourse based extractive summarization methods use sentence similarity to extract informative sentences. This paper presents an innovative application of semigraph which includes the processes of semigraph construction and sentence ...
Sheetal Sonawane +3 more
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EUTS: Extractive Urdu Text Summarizer
2018 Seventeenth Mexican International Conference on Artificial Intelligence (MICAI), 2018Automatic text summarization is a growing area of natural language processing research. Using extractive text summarization approach a concise summary of the input information sources is developed by selecting phrases and sentences on a given selection criterion that can be based on features e.g. syntactic, semantic, temporal, positional, etc.
Aslam Muhammad +3 more
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Abstractive Summarizers are Excellent Extractive Summarizers
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023Daniel Varab, Yumo Xu
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Automated extractive single-document summarization
Proceedings of the 2011 ACM Symposium on Applied Computing, 2011Single document summarization, which is as important as multiple document summarization for a variety of reasons, has been attracting declining interest recently. The goal of this study is to introduce a new approach to single document summarization and its implementation, SynSem.
Araly Barrera, Rakesh Verma
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