Survey of Query-based Text Summarization
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Abstract (arXiv)
Query-based text summarization is an important real world problem that requires to condense the prolix text data into a summary under the guidance of the query information provided by users. The topic has been studied for a long time and there are many existing interesting research related to query-based text summarization. Yet much of the work is not systematically surveyed. This survey aims at summarizing some interesting work in query-based text summarization methods as well as related generic text summarization methods. Not all taxonomies in this paper exist the related work to the best of our knowledge and some analysis will be presented.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 17 Sep 2022.
Builds on
17 of 37 references.
References · 17
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
- 2015Teaching Machines to Read and Comprehendcited by 1,049
- 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
- 2015Pointer Networkscited by 895
- 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
- 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 396
5 more
- 2019Fine-tune BERT for Extractive Summarizationcited by 122
- 2019HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarizationcited by 121
- 2016Modeling Coverage for Neural Machine Translationcited by 110
- 2017Diversity driven Attention Model for Query-based Abstractive Summarizationcited by 42
- 2020Unsupervised Extractive Summarization by Pre-training Hierarchical Transformerscited by 16
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Citing papers per year: 2024: 2.
2 papers.