Filters








26,278 Hits in 4.4 sec

The role of statistical and semantic features in single-document extractive summarization

Tatiana Vodolazova
2013 Artificial intelligence research  
This paper reports on the further results of the ongoing research analyzing the impact of a range of commonly used statistical and semantic features in the context of extractive text summarization.  ...  The obtained results demonstrate the relative importance of each feature and the limitations of the tools available.  ...  Conclusions and future work The goal of the present research is to study the interaction between a set of statistical and semantic features and their impact on the process of extractive text summarization  ... 
doi:10.5430/air.v2n3p35 fatcat:7nfvz43w6rautlsvwiq56lqgmu

A Survey on Various Methodologies of Automatic Text Summarization

Rahul Lahkar, Anup Kumar Barman
2015 International Journal of Engineering Research and  
This survey focuses on some of the existing techniques of statistical document summarization as well as summarization using semantic approaches to deal with the improvements that can be done for Extractive  ...  Automatic text summarization reduces human effort in generating summary from text document(s) with the help of computer program.  ...  The concept of this positional feature can play a great role in extractive summarization.  ... 
doi:10.17577/ijertv4is040341 fatcat:dru5xin24vhzzh3yhad7xqsum4

Automatic summarization of Malayalam documents using clause identification method

Sunitha C, A Jaya, Amal Ganesh
2019 International Journal of Electrical and Computer Engineering (IJECE)  
Extractive summarization selects important sentences from the text and produces summary as it is present in the original document.  ...  The score of each clause is then calculated by using feature extraction and the important clauses which are to be included in the summary are selected based on this score.  ...  Summarization can be classified into various categories, Extractive summarization and Abstractive Summarization, Single document and Multi document summarization, Generic and Query based summarization  ... 
doi:10.11591/ijece.v9i6.pp4929-4938 fatcat:fe5smhjf2bcldgcigf75sx4ssu

A Frequent Term and Semantic Similarity based Single Document Text Summarization Algorithm

Naresh Kumar Nagwani, Shrish Verma
2011 International Journal of Computer Applications  
Finally in the third step all the sentences in the document, which are containing the frequent and semantic equivalent terms, are filtered for summarization.  ...  Text summarization is an important activity in the analysis of a high volume text documents.  ...  The overall methodology of semantic similarity bases single document summarization can be expressed in terms of an algorithm.  ... 
doi:10.5120/2190-2778 fatcat:6hpb3cpnqjh7fcdjnxubzeybka

A Novel Framework for Semantic OrientedAbstractive Text Summarization

N. Moratanch, S. Chitrakala
2019 Journal of Web Engineering  
The content selection involves semantic based content selection and feature extraction are selected by Genetic Algorithm.  ...  The contribution of our works are Joint Model Predicate Sense Disambiguation and Semantic Role Labelling termed as Joint (PSD+SRL) is proposed to better capture the semantic representation of text.  ...  Extractive summarization intends to extract few sentences from the source document based on some statistical factors or scores using techniques of statistical analysis [24] , various machine learning  ... 
doi:10.13052/jwe1540-9589.1784 fatcat:62zfycbebndrtpja2iqzdragpu

Extractive and Abstractive Text Summarization Techniques

2020 International journal of recent technology and engineering  
The extractive summarization methods rely on topics and centrality of the document. The abstractive techniques transform the sentences based on the language resources available.  ...  This paper deals with the study of extractive as well as abstractive strategies in text summarization.  ...  Extractive and Abstractive Text Summarization Techniques PL.Prabha, M.Parvathy Hence, evaluation plays a vital role in assessing the quality of the summarized document.  ... 
doi:10.35940/ijrte.a2235.059120 fatcat:4bfnvpyaxbbw7apxayo4zkuy7u

Event-Based Summarization of News Articles

2020 Turkish Journal of Electrical Engineering and Computer Sciences  
In the method, the important features of event extraction 6 and summarization methods are analyzed and combined together to extract the summaries from single source news 7 documents.  ...  The proposed summarization process is based on event extraction methods and is called 5 an event-based extractive single-document summarization.  ...  Lin et al. [34] used semantic roles such as, predicates-argument, in query with 11 the ones in the main document and proved that semantic roles performs better than syntactic dependencies. 12 Pai et  ... 
doi:10.3906/elk-1904-98 fatcat:ugwbacbbu5eo5g3jifyqhfcrr4

Trends in Extractive and Abstractive Techniques in Text Summarization

Neelima Bhatia, Arunima Jaiswal
2015 International Journal of Computer Applications  
Extractive summarization uses statistical and linguistic features to determine the important features and fuse them into a shorter version.  ...  Whereas abstractive summarization understands the whole document and then generates the summary. In this paper extractive and abstractive methods are framed.  ...  Earlier the summarization system was fed to only a single document. But with the bulk of information it moved to multi documents.  ... 
doi:10.5120/20559-2947 fatcat:n45qldsgobd65lmnugl3lson44

Automatic Summarization of Arabic Texts

2009 Journal of the ACS Advances in Computer Science  
In this article, the linguistic and statistical approaches used in text summarization are presented. Statistical approach is adopted to build an Arabic text summarization system.  ...  Primary subjective evaluation, based on Compression Ratio and Retention Ratio, showed that the used approach is effective and efficient, and performance of the system is promising.  ...  Abstractive approaches to single document summarization address this problem by editing the extracted sentences.  ... 
doi:10.21608/asc.2009.158219 fatcat:3ksmwopazjf3jislmejvpzanke

Hybrid Approach for Single Text Document Summarization using Statistical and Sentiment Features [article]

Chandra Shekhar Yadav, Aditi Sharan
2016 arXiv   pre-print
We are proposing a hybrid model for a single text document summarization. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique.  ...  play a vital role in text document summarization.  ...  Payal Biswas to generate extractive type summary from given document , and Ashish Kumar (All from SC & SS, IR-LAB 01, JNU, Delhi) to help me at several stages.  ... 
arXiv:1601.00643v1 fatcat:fg3sjgitmnginl2lchp7zjmq4a

Review of Automatic Text Summarization Techniques & Methods

Adhika Pramita Widyassari, Supriadi Rustad, Guruh Fajar Shidik, Edi Noersasongko, Abdul Syukur, Affandy Affandy, De Rosal Ignatius Moses Setiadi
2020 Journal of King Saud University: Computer and Information Sciences  
features, techniques, methods, evaluations, and problems in this field of research.  ...  This paper provides a broad and systematic review of research in the field of text summarization published from 2008 to 2019.  ...  The weakness of this system is the semantic problem. Future work can add semantic features by labeling semantic roles and lexical databases and applying this method for multi-document summarizing.  ... 
doi:10.1016/j.jksuci.2020.05.006 fatcat:2u3jd3ounfa6bh6coo342egnly

An Automatic Linguistics Approach for Persian Document Summarization

Hossein Kamyar, Mohsen Kahani, Mohsen Kamyar, Asef Poormasoomi
2011 2011 International Conference on Asian Language Processing  
In most summarization approaches, the major consideration is the statistical properties of text elements such as term frequency.  ...  __ In this paper we propose a novel technique for summarizing a text based on the linguistics properties of text elements and semantic chains among them.  ...  Acknowledgment This work was supported by the Web Technology Laboratory of Ferdowsi University of Mashhad. We would like to thank WTLAB group members.  ... 
doi:10.1109/ialp.2011.52 dblp:conf/ialp/KamyarKKP11 fatcat:u6bgg7umozf4tib4jyxe4lkuuy

Semantic argument frequency-based multi-document summarization

Cem Aksoy, Ahmet Bugdayci, Tunay Gur, Ibrahim Uysal, Fazli Can
2009 2009 24th International Symposium on Computer and Information Sciences  
In this paper, we introduce and assess the idea of using SRL on generic Multi-Document Summarization (MDS).  ...  Semantic Role Labeling (SRL) aims to identify the constituents of a sentence, together with their roles with respect to the sentence predicates.  ...  ACKNOWLEDGMENT We would like to thank the distributors of ASSERT software and ROUGE evaluation package.  ... 
doi:10.1109/iscis.2009.5291878 dblp:conf/iscis/AksoyBGUC09 fatcat:l6f7qevh3nhfxb5rc76gkau76m

Sentiment analysis

Amitava Das, Sivaji Bandyopadhyay, Björn Gambäck
2012 Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics - WIMS '12  
An end user might desire an automated at-a-glance presentation of the main points made in a single review or how opinion changes time to time over multiple documents.  ...  The 5W task seeks to extract the semantic constituents in a natural language sentence by distilling it into the answers to the 5W questions: Who, What, When, Where and Why.  ...  Topic-Wise There is clearly a tight connection between extraction of topicbased information from a single document and topic-based summarization of that document, since the information that is pulled out  ... 
doi:10.1145/2254129.2254173 dblp:conf/wims/DasBG12 fatcat:hab636zklvgavlvz67qpepkutq

A Semantic Approach to Summarization [article]

Divyanshu Bhartiya, Ashudeep Singh
2014 arXiv   pre-print
Sentence extraction based summarization methods has some limitations as it doesn't go into the semantics of the document.  ...  We involve semantic role labeling to get the semantic representation of text and use of segmentation to form clusters of the related pieces of text.  ...  For summarization, McKeown and Radev, (1995) emphasized on using semantic structure of text rather than statistics of words of the documents.  ... 
arXiv:1406.1203v1 fatcat:ejyjf6soijc33cdycf7qzr4kcy
« Previous Showing results 1 — 15 out of 26,278 results