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Exploring actor–object relationships for query-focused multi-document summarization
2014
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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Abstract Most research on multi-document summarization explores methods that generate summaries based on queries regardless of the users' preferences. ...
Keyword User-based summarization · Actor-object relationship · Multi-document summarization · Ensemble summarizing system · Training data construction Communicated by V. Loia. M. Valizadeh (B) · P. ...
the FCT-Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project "FCOMP-01-0124-FEDER-022701" ...
doi:10.1007/s00500-014-1471-x
fatcat:g34zt44e55alrp5s5x6sk7ufj4
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
[article]
2016
arXiv
pre-print
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We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization. ...
We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. ...
We thank Ding-Jung Han, Young-Suk Lee, Xiaoqiang Luo, Sameer Maskey, Myle Ott, Salim Roukos, Yiye Ruan, Ming Tan, Todd Ward, Bowen Zhou, and the ACL reviewers for valuable suggestions and advice on various ...
arXiv:1606.07548v1
fatcat:62kfypjkczf5npg2ypq7x76xha
Automatic Text Summarization
2017
International Journal of Computer Applications
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Automatic text summarization system generates a summary, i.e. it contains short length text which comprises all the key information of the document. ...
Summarization is the art of abstracting key content from one or more information sources [6] . Summarization includes text summarization, image summarization, and video summarization. ...
Large amount of annotated or labeled data is needed for learning techniques. ...
doi:10.5120/ijca2017912326
fatcat:4vo5tlgakbbshc6fd2zilxusve
Document Summarization with Latent Queries
2022
Transactions of the Association for Computational Linguistics
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For query-focused summarization (QFS), labeled training data in the form of queries, documents, and summaries is not readily available. ...
Despite learning from generic summarization data only, our approach outperforms strong comparison systems across benchmarks, query types, document settings, and target domains.1 ...
Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. ...
doi:10.1162/tacl_a_00480
fatcat:xcfvrc5mtvdifi72ngu4wpsm4y
Live blog summarization
2021
Language Resources and Evaluation
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empirically evaluating well-known state-of-the-art unsupervised and supervised summarization systems on our new corpus. ...
In this article, (a) we first define the task of summarizing a live blog, (b) study ways of automatically collecting corpora for live blog summarization, and (c) understand the complexity of the task by ...
., topics) and 186,999 postings (i.e., documents). With that many data points, machine learning approaches become readily applicable. ...
doi:10.1007/s10579-020-09513-5
fatcat:2y6owouoivg4vdscyc7ygb4asy
Automatic Keyword Extraction for Text Summarization: A Survey
[article]
2017
arXiv
pre-print
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Due to the excessiveness of data, there is a need of automatic summarizer which will be capable to summarize the data especially textual data in original document without losing any critical purposes. ...
In this paper, recent literature on automatic keyword extraction and text summarization are presented since text summarization process is highly depend on keyword extraction. ...
The learning process was supervised, it used human annotated keyword set to train the model. Mirroshandel et al. ...
arXiv:1704.03242v1
fatcat:poa2yh2uhbcgfaemgfqa5ylxim
An Overview of Text Summarization
2017
International Journal of Computer Applications
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This paper presents a comprehensive survey of contemporary text summarization of extractive and abstractive approaches. ...
Automatic text summarization system produces a summary, i.e. short length text that includes all the significant information for the article. ...
[103] approached the problem of automatically generating summary from medical article as a supervised learning task. ...
doi:10.5120/ijca2017915109
fatcat:hlpvgrzpd5hord5bfy4zndqexi
APPROACHES TO AUTOMATIC SUMMARIZATION AND ANNOTATION
2021
Науковий вісник ДДПУ імені Івана Франка. Серія: Філологічні науки (мовознавство)
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The article is devoted to the study of different approaches of digital texts automatic summarization and annotation. ...
devoted to the development of methods and approaches in automatic summarization and annotation have become and are still relevant and really vital. ...
Now let us study the approaches of automatic summarization. So, some authors also divide automatic summarization and annotation methods into surface-level and deep learning methods [17] . ...
doi:10.24919/2663-6042.16.2021.2
fatcat:5637fpad5rfonmebw7eq3tlrfi
OHSU Summarization and Entity Linking Systems
2009
Text Analysis Conference
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For the first, we use the same general machine learning approach described in Fisher and Roark (2008) for update summarization. ...
We first present two supervised sentence ranking approaches for use in extractive update summarization. ...
We examine this approach within the context of query-focused multi-document summarization, for which there is much less training data for supervised approaches than query-neutral multi-document summarization ...
dblp:conf/tac/FisherDRCB09
fatcat:bk5luefhmbaevk7bagrircn5ry
Query-Focused Extractive Video Summarization
[article]
2016
arXiv
pre-print
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We verify our approach on two densely annotated video datasets. The query-focused video summarization is particularly useful for search engines, e.g., to display snippets of videos. ...
Video data is explosively growing. As a result of the "big video data", intelligent algorithms for automatic video summarization have re-emerged as a pressing need. ...
We name it query-focused (extractive) video summarization, in accordance with the query-focused document summarization [29] in NLP. ...
arXiv:1607.05177v1
fatcat:5cnie3dj3zfqtlbh3mj2nnzfuq
Transforming Wikipedia into Augmented Data for Query-Focused Summarization
[article]
2022
arXiv
pre-print
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The limited size of existing query-focused summarization datasets renders training data-driven summarization models challenging. ...
We also develop a BERT-based query-focused summarization model (Q-BERT) to extract sentences from the documents as summaries. ...
[36] propose SIBERT that extends HIBERT [37] to query-focused multi-document summarization by introducing a cross-document infusion layer and incorporating queries as additional contexts. ...
arXiv:1911.03324v2
fatcat:ofoyvu2tvzazjoomvo2u5ot7ju
Text Summarization Techniques: A Brief Survey
2017
International Journal of Advanced Computer Science and Applications
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In this review, the main approaches to automatic text summarization are described. ...
Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. ...
In semi-supervised learning we utilize the unlabeled data in training. There is usually a small amount of labeled data along with a large amount of unlabeled data. ...
doi:10.14569/ijacsa.2017.081052
fatcat:ljmnyusgdrfypmyjgqsxlvulwi
Text Summarization Techniques: A Brief Survey
[article]
2017
arXiv
pre-print
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In this review, the main approaches to automatic text summarization are described. ...
We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods. ...
LDA has been extensively used for multi-document summarization recently. For example, Daume et al. [19] proposed B S , a Bayesian summarization model for query-focused summarization. Wang et al. ...
arXiv:1707.02268v3
fatcat:mq77jgh56ra57jjhf4s5lqq7ie
Review on automatic text summarization
2018
International Journal of Engineering & Technology
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Due to the abundant information available in different forms of sources and genres, there is an immense need to summarize the data for humans. ...
Past few years have witnessed a rapid growth in the research of summarizing the text automatically using different approaches. ...
Many supervised, unsupervised and semi supervised machine learning algorithms like Naïve Bayes(NB), Random Forests or Decision Trees, Hidden Markov Models(HMM), Conditional Random Fields(CRF), Support ...
doi:10.14419/ijet.v7i2.33.14210
fatcat:z7lffdxnwnejlgcytgs24va4mq
Coarse-to-Fine Query Focused Multi-Document Summarization
2020
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
unpublished
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We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization. ...
The modules can be independently developed and leverage training data if available. ...
Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation therein. ...
doi:10.18653/v1/2020.emnlp-main.296
fatcat:gtepbzzhvfcotpurbymk4lym5u
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