• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Comparative Analysis of Approaches to Extractive Text Summarization

Student: Don Stanislav

Supervisor: Konstantin Y. Degtyarev

Faculty: Faculty of Computer Science

Educational Programme: Software Engineering (Bachelor)

Year of Graduation: 2020

With the increasing amount of information available on the Internet, text summarization has become an important and more popular area for study. Hence, many algorithms have been proposed. The primary purpose of this work is to present an overview of existing approaches for extractive text summarization and make a clear comparison between them. In particular, we compare 5 different groups of algorithms: traditional feature-based, graph-based, cluster-based, fuzzy-logic based, and our new approach based on text-filtering and clustering. The proposed technique is developed to increase summary diversity. The comparison will be achieved by experimentations conducted on the DUC2002 and BBC News Summary datasets.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses