Results 41 to 50 of about 4,298,648 (328)
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents.
Ye Liu +5 more
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Survey of Deep Learning Based Extractive Summarization [PDF]
Jisuanji kexue yu tansuoAutomatic text summarization (ATS) is a popular research direction in natural language processing, and its main implementation methods are divided into two categories: extractive and abstractive. Extractive summarization directly uses the text content in
TIAN Xuan, LI Jialiang, MENG Xiaohuan
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Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2023 Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multidocument, where one place has several reviews from different users.
Narandha Arya Ranggianto +3 more
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Sliding Selector Network with Dynamic Memory for Extractive Summarization of Long Documents
North American Chapter of the Association for Computational Linguistics, 2021 Neural-based summarization models suffer from the length limitation of text encoder. Long documents have to been truncated before they are sent to the model, which results in huge loss of summary-relevant contents.
Peng Cui, Le Hu
semanticscholar +1 more source
Multi-Task Learning for Abstractive and Extractive Summarization
Data Science and Engineering, 2019 The abstractive method and extractive method are two main approaches for automatic document summarization. In this paper, to fully integrate the relatedness and advantages of both approaches, we propose a general unified framework for abstractive ...
Yangbin Chen +3 more
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Deep Extractive Text Summarization
Procedia Computer Science, 2020 Abstract With introduction of deep learning techniques their has been an increase in intelligent classification of text in many applications. Advances in automatic text summarization using deep learning technique is prime focus of research now a days.
Rupal Bhargava, Yashvardhan Sharma
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Using NLP to Generate MARC Summary Fields for Notre Dame ’s Catholic Pamphlets
International Journal of Librarianship, 2020 Three NLP (Natural Language Processing) automated summarization techniques were tested on a special collection of Catholic Pamphlets acquired by Hesburgh Libraries.
Jeremiah Flannery
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Journal of Hebei University of Science and Technology, 2020 In order to solve the problem of ineffective utilization of the semantic information between documents in the traditional multi-document extractive summarization method and the excessive redundant content in the summary result, a Khmer multi-document ...
Zhaolin ZENG +4 more
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Multi-class extractive voicemail summarization
8th European Conference on Speech Communication and Technology (Eurospeech 2003), 2003 This paper is about a system that extracts principal content words from speech-recognized transcripts of voicemail messages and classifies them into proper names, telephone numbers, dates/times and `other'. The short text summaries generated are suitable for mobile messaging applications.
Koumpis, Konstantinos, Renals, Steve
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Multi-document summarization by sentence extraction [PDF]
NAACL-ANLP 2000 Workshop on Automatic Summarization -, 2000 This paper discusses a text extraction approach to multi-document summarization that builds on single-document summarization methods by using additional, available information about the document set as a whole and the relationships between the documents.
Goldstein, Jade +3 more
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