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Methods of Text Summarization and Extracting Key Information From Russian News Articles

Student: Vasiliy Vdovkin

Supervisor: Fedor Vladimirovich Strok

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Information Science and Computation Technology (Bachelor)

Final Grade: 7

Year of Graduation: 2018

This work describes implementation of the service that can significantly improve user experience in news content consumption by using Natural Language Processing (NLP for short). This service analyses stream of news articles from russian media web-sites in real-time, groups news by events and extracts the most valuable information about the events. To implement this service we first research and then use models and methods from NLP to find suitable solution for the following problems: normalization, vectorization, clusterization and summarization of text. Clusterization allows us to automatically group news by events. In order for this to work, we collect the data from web-sites of media and train TF-IDF model allows to use K-means algorithm for news clusterization. Summarization depends on SimBasic and DivRank models and extracts the most informative sentences from the event-cluster. We compare and evaluate the proposed solutions.

Full text (added May 15, 2018)

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