{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:57:01Z","timestamp":1777705021101,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,11,11]]},"abstract":"<jats:p>Dealing with the explosive growth of web sources on the Internet requires the use of efficient systems. Automatic text summarization is capable of addressing this issue. Recent years have seen remarkable success in the use of graph theory on text extractive summarization. However, the understanding of why and how they perform so well is still not clear. In this paper, we intend to seek a better understanding of graph models, which can benefit from graph extractive summarization. Additionally, analysis has been performed qualitatively with the graph models in the design of recent graph extractive summarization. Based on the knowledge acquired from the survey, our work could provide more clues for future research on extractive summarization.<\/jats:p>","DOI":"10.3233\/jifs-220433","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T12:38:16Z","timestamp":1654605496000},"page":"7057-7065","source":"Crossref","is-referenced-by-count":0,"title":["What we achieve on text extractive summarization based on graph?"],"prefix":"10.1177","volume":"43","author":[{"given":"Shuang","family":"Chen","sequence":"first","affiliation":[{"name":"Software College, Northeastern University, Hunnan District, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Ren","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Hunnan District, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Qv","sequence":"additional","affiliation":[{"name":"Software College, 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