Summarizing News Paper Articles: Experiments with Ontology- Based, Customized, Extractive Text Summary and Word Scoring
The method for filtering information from large volumes of text is called Information Extraction. It is a limited task than understanding the full text. In full text understanding, we express in an explicit fashion about all the information in a given text. But, in Information Extraction, we delimit...
Gespeichert in:
| Veröffentlicht in: | Cybernetics and information technologies : CIT 2012-01, Vol.12 (2), p.34-50 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 50 |
|---|---|
| container_issue | 2 |
| container_start_page | 34 |
| container_title | Cybernetics and information technologies : CIT |
| container_volume | 12 |
| creator | Kallimani, Jagadish S. Srinivasa, K. G. Eswara Reddy, B. |
| description | The method for filtering information from large volumes of text is called Information Extraction. It is a limited task than understanding the full text. In full text understanding, we express in an explicit fashion about all the information in a given text. But, in Information Extraction, we delimit in advance, as part of the specification of the task and the semantic range of the result. Only extractive summarization method is considered and developed for the study. In this article a model for summarization from large documents using a novel approach has been proposed by considering one of the South Indian regional languages (Kannada). It deals with a single document summarization based on statistical approach. The purpose of summary of an article is to facilitate the quick and accurate identification of the topic of the published document. The objective is to save prospective readers’ time and effort in finding the useful information in a given huge article. Various analyses of results were also discussed by comparing it with the English language. |
| doi_str_mv | 10.2478/cait-2012-0011 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1686435206</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3154854283</sourcerecordid><originalsourceid>FETCH-LOGICAL-c378t-7db77b32d55d2b03a579c5a3e7c337be79302e329687a0f807635207e2ededc53</originalsourceid><addsrcrecordid>eNpdkc1PGzEQxVcVSCDKlbOlXjjg1B_rtbc3iNKPCBVagjhajncCht11sL1N4K-vt6k44ItnpN97M_YrihNKJqyU6rM1LmFGKMOEUPqhOKSclrgkiu79qymuJWEHxXGMjySfsuZUicNiczN0nQnu1fX36CdsIro2awjoPCRnW4hf0Gybe9dBnyLauPSArvrkW3__gtGFidCcoekQk-_c61jPtikYm9wfQAvYJrSzf0Gmb9CdDw26sT7kWR-L_ZVpIxz_v4-K26-zxfQ7vrz69mN6foktlyph2SylXHLWCNGwJeFGyNoKw0FazuUSZM0JA87qSklDVorIigtGJDBooLGCHxWnO9918M8DxKQ7Fy20renBD1HTSlXlKKky-ukd-uiH0OftNKeiVKJkimdqsqNs8DEGWOl1_p38RE2JHqPQYxR6jEKPUWQB3glcTLB9o0140pXkUuhfi1L_nte1oHOp5_wvX1eLNQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3154854283</pqid></control><display><type>article</type><title>Summarizing News Paper Articles: Experiments with Ontology- Based, Customized, Extractive Text Summary and Word Scoring</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Kallimani, Jagadish S. ; Srinivasa, K. G. ; Eswara Reddy, B.</creator><creatorcontrib>Kallimani, Jagadish S. ; Srinivasa, K. G. ; Eswara Reddy, B.</creatorcontrib><description>The method for filtering information from large volumes of text is called Information Extraction. It is a limited task than understanding the full text. In full text understanding, we express in an explicit fashion about all the information in a given text. But, in Information Extraction, we delimit in advance, as part of the specification of the task and the semantic range of the result. Only extractive summarization method is considered and developed for the study. In this article a model for summarization from large documents using a novel approach has been proposed by considering one of the South Indian regional languages (Kannada). It deals with a single document summarization based on statistical approach. The purpose of summary of an article is to facilitate the quick and accurate identification of the topic of the published document. The objective is to save prospective readers’ time and effort in finding the useful information in a given huge article. Various analyses of results were also discussed by comparing it with the English language.</description><identifier>ISSN: 1311-9702</identifier><identifier>EISSN: 1314-4081</identifier><identifier>DOI: 10.2478/cait-2012-0011</identifier><language>eng</language><publisher>Sofia: Versita</publisher><subject>automatic text summarization ; Customizing ; extractive summarization ; Filtering ; Information extraction ; Information retrieval ; Scoring ; Semantics ; stemming ; Summaries ; Tasks ; text summarization ; Texts ; UTF-8 ; word count frequency</subject><ispartof>Cybernetics and information technologies : CIT, 2012-01, Vol.12 (2), p.34-50</ispartof><rights>20120101.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c378t-7db77b32d55d2b03a579c5a3e7c337be79302e329687a0f807635207e2ededc53</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids></links><search><creatorcontrib>Kallimani, Jagadish S.</creatorcontrib><creatorcontrib>Srinivasa, K. G.</creatorcontrib><creatorcontrib>Eswara Reddy, B.</creatorcontrib><title>Summarizing News Paper Articles: Experiments with Ontology- Based, Customized, Extractive Text Summary and Word Scoring</title><title>Cybernetics and information technologies : CIT</title><description>The method for filtering information from large volumes of text is called Information Extraction. It is a limited task than understanding the full text. In full text understanding, we express in an explicit fashion about all the information in a given text. But, in Information Extraction, we delimit in advance, as part of the specification of the task and the semantic range of the result. Only extractive summarization method is considered and developed for the study. In this article a model for summarization from large documents using a novel approach has been proposed by considering one of the South Indian regional languages (Kannada). It deals with a single document summarization based on statistical approach. The purpose of summary of an article is to facilitate the quick and accurate identification of the topic of the published document. The objective is to save prospective readers’ time and effort in finding the useful information in a given huge article. Various analyses of results were also discussed by comparing it with the English language.</description><subject>automatic text summarization</subject><subject>Customizing</subject><subject>extractive summarization</subject><subject>Filtering</subject><subject>Information extraction</subject><subject>Information retrieval</subject><subject>Scoring</subject><subject>Semantics</subject><subject>stemming</subject><subject>Summaries</subject><subject>Tasks</subject><subject>text summarization</subject><subject>Texts</subject><subject>UTF-8</subject><subject>word count frequency</subject><issn>1311-9702</issn><issn>1314-4081</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdkc1PGzEQxVcVSCDKlbOlXjjg1B_rtbc3iNKPCBVagjhajncCht11sL1N4K-vt6k44ItnpN97M_YrihNKJqyU6rM1LmFGKMOEUPqhOKSclrgkiu79qymuJWEHxXGMjySfsuZUicNiczN0nQnu1fX36CdsIro2awjoPCRnW4hf0Gybe9dBnyLauPSArvrkW3__gtGFidCcoekQk-_c61jPtikYm9wfQAvYJrSzf0Gmb9CdDw26sT7kWR-L_ZVpIxz_v4-K26-zxfQ7vrz69mN6foktlyph2SylXHLWCNGwJeFGyNoKw0FazuUSZM0JA87qSklDVorIigtGJDBooLGCHxWnO9918M8DxKQ7Fy20renBD1HTSlXlKKky-ukd-uiH0OftNKeiVKJkimdqsqNs8DEGWOl1_p38RE2JHqPQYxR6jEKPUWQB3glcTLB9o0140pXkUuhfi1L_nte1oHOp5_wvX1eLNQ</recordid><startdate>20120101</startdate><enddate>20120101</enddate><creator>Kallimani, Jagadish S.</creator><creator>Srinivasa, K. G.</creator><creator>Eswara Reddy, B.</creator><general>Versita</general><general>De Gruyter Poland</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20120101</creationdate><title>Summarizing News Paper Articles: Experiments with Ontology- Based, Customized, Extractive Text Summary and Word Scoring</title><author>Kallimani, Jagadish S. ; Srinivasa, K. G. ; Eswara Reddy, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c378t-7db77b32d55d2b03a579c5a3e7c337be79302e329687a0f807635207e2ededc53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>automatic text summarization</topic><topic>Customizing</topic><topic>extractive summarization</topic><topic>Filtering</topic><topic>Information extraction</topic><topic>Information retrieval</topic><topic>Scoring</topic><topic>Semantics</topic><topic>stemming</topic><topic>Summaries</topic><topic>Tasks</topic><topic>text summarization</topic><topic>Texts</topic><topic>UTF-8</topic><topic>word count frequency</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kallimani, Jagadish S.</creatorcontrib><creatorcontrib>Srinivasa, K. G.</creatorcontrib><creatorcontrib>Eswara Reddy, B.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Cybernetics and information technologies : CIT</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kallimani, Jagadish S.</au><au>Srinivasa, K. G.</au><au>Eswara Reddy, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Summarizing News Paper Articles: Experiments with Ontology- Based, Customized, Extractive Text Summary and Word Scoring</atitle><jtitle>Cybernetics and information technologies : CIT</jtitle><date>2012-01-01</date><risdate>2012</risdate><volume>12</volume><issue>2</issue><spage>34</spage><epage>50</epage><pages>34-50</pages><issn>1311-9702</issn><eissn>1314-4081</eissn><abstract>The method for filtering information from large volumes of text is called Information Extraction. It is a limited task than understanding the full text. In full text understanding, we express in an explicit fashion about all the information in a given text. But, in Information Extraction, we delimit in advance, as part of the specification of the task and the semantic range of the result. Only extractive summarization method is considered and developed for the study. In this article a model for summarization from large documents using a novel approach has been proposed by considering one of the South Indian regional languages (Kannada). It deals with a single document summarization based on statistical approach. The purpose of summary of an article is to facilitate the quick and accurate identification of the topic of the published document. The objective is to save prospective readers’ time and effort in finding the useful information in a given huge article. Various analyses of results were also discussed by comparing it with the English language.</abstract><cop>Sofia</cop><pub>Versita</pub><doi>10.2478/cait-2012-0011</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1311-9702 |
| ispartof | Cybernetics and information technologies : CIT, 2012-01, Vol.12 (2), p.34-50 |
| issn | 1311-9702 1314-4081 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1686435206 |
| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | automatic text summarization Customizing extractive summarization Filtering Information extraction Information retrieval Scoring Semantics stemming Summaries Tasks text summarization Texts UTF-8 word count frequency |
| title | Summarizing News Paper Articles: Experiments with Ontology- Based, Customized, Extractive Text Summary and Word Scoring |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-14T23%3A34%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Summarizing%20News%20Paper%20Articles:%20Experiments%20with%20Ontology-%20Based,%20Customized,%20Extractive%20Text%20Summary%20and%20Word%20Scoring&rft.jtitle=Cybernetics%20and%20information%20technologies%20:%20CIT&rft.au=Kallimani,%20Jagadish%20S.&rft.date=2012-01-01&rft.volume=12&rft.issue=2&rft.spage=34&rft.epage=50&rft.pages=34-50&rft.issn=1311-9702&rft.eissn=1314-4081&rft_id=info:doi/10.2478/cait-2012-0011&rft_dat=%3Cproquest_cross%3E3154854283%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3154854283&rft_id=info:pmid/&rfr_iscdi=true |