Multidocument Summarization

Student: Kulikov Aleksandr

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

In this paper, various methods for solving the problem of summation of documents were studied and developed. A set of data about book reviews was used as data for testing. In the work considered in detail two models: Seq2Seq and BertSum. The first model showed good results for the abstract generalization problem. She identified the nature of the reviews and summarized the main message. However, reviews that have many phrases with ambiguous interpretations are poorly summarized. The second model solved the problem of extractive summation well. The result is a short version of the reviews with the most important information. This work contains 22 pages, 4 chapters, 7 drawings, 7 references. Keywords – document summarization, NLP, Seq2seq LSTM, BERTSum.

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