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A Text Feature Based Automatic Keyword Extraction Method for Single Documents [chapter]

Ricardo Campos, Vítor Mangaravite, Arian Pasquali, Alípio Mário Jorge, Célia Nunes, Adam Jatowt
2018 Lecture Notes in Computer Science  
In this work, we propose a lightweight approach for keyword extraction and ranking based on an unsupervised methodology to select the most important keywords of a single document.  ...  The experimental results suggest that extracting keywords from documents using our method results in a superior effectiveness when compared to similar approaches.  ...  which builds upon text statistical features, to extract keywords (both single-word and multi-word terms) from single documents, thus without the need to rely on a document collection; (2) YAKE!  ... 
doi:10.1007/978-3-319-76941-7_63 fatcat:ptqjoc3pmfajppcivijzskonji

Automatic Keyword Extraction for Text Summarization: A Survey [article]

Santosh Kumar Bharti, Korra Sathya Babu
2017 arXiv   pre-print
In this paper, recent literature on automatic keyword extraction and text summarization are presented since text summarization process is highly depend on keyword extraction.  ...  This literature includes the discussion about different methodology used for keyword extraction and text summarization.  ...  [5] designed a system for automatic keyword extraction for text summarization in single document e-Newspaper article. Marcu et al.  ... 
arXiv:1704.03242v1 fatcat:poa2yh2uhbcgfaemgfqa5ylxim

Predicting Abstract Keywords by Word Vectors [chapter]

Qing Li, Wenhao Zhu, Zhiguo Lu
2016 Lecture Notes in Computer Science  
To solve it, this paper proposes a keyword extraction method based on word vectors. The concept of a text turns into computer understandable space by training word vectors using a word2vec algorithm.  ...  The Euclidean distances between every candidate words and every text words are calculated to find out the top-N-closest keywords as the automatic text extraction keywords.  ...  For the single keywords, in the two automatically extracted words, the correct number is one, so P is 50 % while the manual selection of single keywords is one, the recall rate R is 100 %; Based on P and  ... 
doi:10.1007/978-3-319-32557-6_20 fatcat:2jj2v63wkvabxbnoviyuvcvxy4


T. N. Moskvitina, South Ural State Humanitarian Pedagogical University
2018 Vestnik Tomskogo Gosudarstvennogo Pedagogičeskogo Universiteta  
In this paper, we proposed a hybrid approach to extract keyword automatically for multi-document text summarization in enewspaper articles.  ...  Here, an algorithm is proposed for automatic keyword extraction for text summarization.  ...  Thomas et al [6] designed a system for automatic keyword extraction for text summarization in single document e-Newspaper article.  ... 
doi:10.23951/1609-624x-2018-8-45-50 fatcat:zyhcnwwkprdl7akq4tu2hgz2we

Single document keywords extraction in Bahasa Indonesia using phrase chunking

I Nyoman Prayana Trisna, Arif Nurwidyantoro
2020 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
This research focused on generating keywords from a document automatically using phrase chunking. Firstly, we collected part of speech patterns from a collection of documents.  ...  Secondly, we used those patterns to extract candidate keywords from the abstract and the content of a document.  ...  methods to extract keywords automatically in a single document.  ... 
doi:10.12928/telkomnika.v18i4.14389 fatcat:zfir6uka2bb4rj3qlppiy6awny

Hybrid Algorithm to Generate Summary of Documents by Extracting Keywords

Devanshi Parikh, Surbhi Patel, Dr. Hiren Joshi, Rollwala Computer Center, Gujarat University, Ahmedabad, India
2020 International Journal of Engineering Research and  
, and "Keywords" in a document represents subset of words or phrases from the document for describing its meaning.  ...  Since text summarization process is highly depend on keyword extraction, the overall results are found promising.  ...  A novel statistical method to perform an extractive text summarization on single document is demonstrated.  ... 
doi:10.17577/ijertv9is040484 fatcat:7j5os5gbpjd3hj7udil2m67fde

A Review Paper on Automatic Text Summarization in Indonesia Language

Nurul Khotimah, Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480., Adi Wibowo P, Bryan Andreas, Abba Suganda Girsang
2021 International Journal of Emerging Technology and Advanced Engineering  
Text summarization is one problem in natural language processing that generates a brief version of the original document.  ...  This paper shows some methods details and summarize the results. KeywordsText summarization, extractive summary, abstractive summary, natural language processing  ...  Single document text summarization is built by produce produce a single output document [4] .  ... 
doi:10.46338/ijetae0821_11 fatcat:clqguhupejfklcztgzmyrnvxdq

Using the Ship-Gram Model for Japanese Keyword Extraction Based on News Reports

Miao Teng, Zhihan Lv
2021 Complexity  
In this paper, we conduct an in-depth study of Japanese keyword extraction from news reports, train external computer document word sets from text preprocessing into word vectors using the Ship-gram model  ...  model methods, respectively; MF-Rank can achieve a maximum performance improvement of 1.76% compared with PW-TF.  ...  Another way is to use automatic keyword extraction technology; i.e., the computer automatically extracts the corresponding keywords from the text according to a certain method.  ... 
doi:10.1155/2021/9965843 fatcat:56nwx6egwfbjjdxgqsgpjke36a

Learning to Extract Folktale Keywords

Dolf Trieschnigg, Dong Nguyen, Mariët Theune
2013 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities  
Manually assigned keywords provide a valuable means for accessing large document collections.  ...  We carry out a quantitative and qualitative analysis of the keywords in the collection. Up to 80% of the assigned keywords (or a minor variation) appear in the text itself.  ...  Acknowledgments This research was supported by the Folktales as Classifiable Texts (FACT) project, part of the CATCH programme funded by the Netherlands Organisation for Scientific Research (NWO).  ... 
dblp:conf/latech/TrieschniggNT13 fatcat:sdtzxlpajnfq7h2bh7af6vyqtq

Impact analysis of keyword extraction using contextual word embedding

Muhammad Qasim Khan, Abdul Shahid, M. Irfan Uddin, Muhammad Roman, Abdullah Alharbi, Wael Alosaimi, Jameel Almalki, Saeed M. Alshahrani
2022 PeerJ Computer Science  
Similarly, context-based features may be beneficial in the job of keyword extraction.  ...  Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part.  ...  Context-based features are pretty helpful for extracting keywords, and this process is intrinsic to extracting the central meaning of a text document and expressing the issues discussed in it.  ... 
doi:10.7717/peerj-cs.967 pmid:35721401 pmcid:PMC9202614 fatcat:4jjq3jqtm5hvfpw6wl7eaj66pe

A boundary-based tokenization technique for extractive text summarization

Nnaemeka M Oparauwah, Juliet N Odii, Ikechukwu I Ayogu, Vitalis C Iwuchukwu
2021 World Journal of Advanced Research and Reviews  
This study presents a boundary-based tokenization method for extractive text summarization. The proposed method performs word tokenization by defining word boundaries in place of specific delimiters.  ...  The need to extract and manage vital information contained in copious volumes of text documents has given birth to several automatic text summarization (ATS) approaches.  ...  Acknowledgments The authors are thankful to the anonymous reviewers of this work for their reviews. Disclosure of conflict of interest The authors declare that no known competing interest exists.  ... 
doi:10.30574/wjarr.2021.11.2.0351 fatcat:t67arfwm3neyzkai7sevgm63uy

A Survey of Automatic Text Summarization System for Different Regional Language in India

Virat V. Giri, Dr.M.M. Math, Dr.U.P. Kulkarni
2016 Bonfring International Journal of Software Engineering and Soft Computing  
This paper concentrates on survey and performance analysis of automatic text summarizers for Marathi language.  ...  The relevant sentences are extracted by applying statistical and language dependent features to the input text.  ...  STEPS FOR TEXT SUMMARIZATION FOR MARATHI LANGUAGE 1. To implement text extraction based Marathi text summarization system for single stories, single news documents and multi news documents. 2.  ... 
doi:10.9756/bijsesc.8242 fatcat:lw3cntpp6vcm5ezcqq5v4rbx4e

Semi-automatic System for Title Construction [article]

Swagata Duari, Vasudha Bhatnagar
2019 arXiv   pre-print
The work is based on the hypothesis that keywords are good candidates for title construction. We extract important words from the document by inducing a supervised keyword extraction model.  ...  The model is trained on novel features extracted from graph-of-text representation of the document.  ...  Our approach do not generate a title, instead it recommends impactful words for inclusion in the title. We design a supervised framework to automatically extract keywords from single documents.  ... 
arXiv:1905.00470v1 fatcat:n4k3vkbspnf3bevarjqavfdm44

A study on automatically extracted keywords in text categorization

Anette Hulth, Beáta B. Megyesi
2006 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06  
This paper presents a study on if and how automatically extracted keywords can be used to improve text categorization.  ...  extracted keywords.  ...  Acknowledgments The authors are grateful to the anonymous reviewers for their valuable suggestions on how to improve the paper.  ... 
doi:10.3115/1220175.1220243 dblp:conf/acl/HulthM06 fatcat:lgelsjrxtja5rhymmdrkyrrbg4

Text Summarization Extraction System (TSES) Using Extracted Keywords

Rafeeq Al-Hashemi
2010 ˜The œinternational Arab journal of e-technology  
In first stage, the system removes the stop words, pars the text and assigning the POS (tag) for each word in the text and store the result in a table.  ...  Each sentence ranked depending on many features such as the existence of the keywords/keyphrase in it, the relation between the sentence and the title by using a similarity measurement and other many features  ...  Two methods for automatic text summarization they are Extractive and Abstractive.  ... 
dblp:journals/iajet/Al-Hashemi10 fatcat:nwzqb6nmkrdg7nqpvbe466tcii
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