{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T10:57:47Z","timestamp":1782385067896,"version":"3.54.5"},"reference-count":17,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2019,2]]},"abstract":"<jats:p> In the era of information overload, text summarization has become a focus of attention in a number of diverse fields such as, question answering systems, intelligence analysis, news recommendation systems, search results in web search engines, and so on. A good document representation is the key point in any successful summarizer. Learning this representation becomes a very active research in natural language processing field (NLP). Traditional approaches mostly fail to deliver a good representation. Word embedding has proved an excellent performance in learning the representation. In this paper, a modified BM25 with Word Embeddings are used to build the sentence vectors from word vectors. The entire document is represented as a set of sentence vectors. Then, the similarity between every pair of sentence vectors is computed. After that, TextRank, a graph-based model, is used to rank the sentences. The summary is generated by picking the top-ranked sentences according to the compression rate. Two well-known datasets, DUC2002 and DUC2004, are used to evaluate the models. The experimental results show that the proposed models perform comprehensively better compared to the state-of-the-art methods. <\/jats:p>","DOI":"10.1142\/s0218194019500086","type":"journal-article","created":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T06:40:21Z","timestamp":1551422421000},"page":"177-196","source":"Crossref","is-referenced-by-count":8,"title":["Document Summarization Using Sentence-Level Semantic Based on Word Embeddings"],"prefix":"10.1142","volume":"29","author":[{"given":"Kamal","family":"Al-Sabahi","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Zuping","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2019,2,28]]},"reference":[{"key":"S0218194019500086BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-016-9475-9"},{"key":"S0218194019500086BIB002","doi-asserted-by":"publisher","DOI":"10.1142\/S0218194017500024"},{"key":"S0218194019500086BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-018-3286-z"},{"key":"S0218194019500086BIB004","first-page":"016555151769353","author":"Shafiee F.","year":"2017","journal-title":"J. Inf. 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