The article on the improvement methods of the summarization quality by the employees of the Institute of Information Technology of ANAS and the scientists of Malaya University (Malaysia) was published in the journal of “Soft Computing. A Fusion of Foundations, Methodologies and Applications” of Springer.

The authors of the article on “Query-based multi-documents summarization using linguistic knowledge and content word expansion”are Asad Abdi, Norisma Idris, Rasim Alguliyev, and Ramiz Aliguliyev.

In this paper, a query-based summarization method, which uses a combination of semantic relations between words and their syntactic composition, to extract meaningful sentences from document sets is introduced. The problem with current statistical methods is that they fail to capture the meaning when comparing a sentence and a user query; hence there is often a conflict between the extracted sentences and users’ requirements. However, this particular method can improve the quality of document summaries because it is able to avoid extracting a sentence whose similarity with the query is high but whose meaning is different. The method is executed by computing the semantic and syntactic similarity of the sentence-to-sentence and sentence-to-query. To reduce redundancy, in summary, this method uses the greedy algorithm to impose diversity penalty on the sentences. In addition, the proposed method expands the words in both the query and the sentences to tackle the problem of information limit. 

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