Open AccessImplementing Supervised Approach to Summarization of Research Papers
Author(s) -
Shaguna Awasth
Publication year - 2020
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst061275
Subject(s) - automatic summarization , computer science , information retrieval , section (typography) , context (archaeology) , encode , prime (order theory) , natural language processing , artificial intelligence , data science , paleontology , biochemistry , chemistry , mathematics , combinatorics , gene , biology , operating system
Using automatic text summarization we can reduce a document to its main information or to what is knownas crux of the document .Recent research in this zone has zeroed in on neural ways to deal withsummarisation, which can be very data hungry.This paper aims to explore a quicker way by implementing a supervised-learning based extractivesummarisation system for the summarisation of research papers.This paper also explores the possibility of any section, in a research paper being the prime section to generatesummaries by utilizing ROUGE scores. An easy to implement and intuitive model is developed using gloveembeddings and doc2vec to encode sentences and documents in their local and global context producinggrammatically coherent summaries.