Text summarization using enhanced MMR technique

Abstract

Now a day when huge amount of documents and web contents are available, so reading of full content is somewhat difficult. Summarization is a way to give abstract form of large document so that the moral of the document can be communicated easily. Current research in automatic summarization is dominated by some effective, yet naive approaches: summarization through extraction, summarization through Abstraction and multi-document summarization. These techniques are used to building a summary of a document. Although there are a number of techniques implemented for the summarization of text for the single document or for the online web data or for any language. Here in this paper we are implemented an efficient technique for text summarization to reduce the computational cost and time and also the storage capacity.

Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline

“…Inspired by PageRank algorithm (Page et al, 1999), they consider the document as a graph where sentences are vertices and edges represent the relations between two sentences. Shortly thereafter, some researchers (Carbonell and Goldstein, 1998;Kurmi and Jain, 2014;Mao et al, 2020) involved a query-biased strategy, the Maximal Marginal Relevance (MMR) (Carbonell and Goldstein, 1998), in their summarizers. MMR tries to balance the relevance and diversity by controlling the trade-off parameter λ.…”

Comparative Study of Bug Report Summarization Techniques

“…In the same year 2014, the creation of summaries at the paragraph level started coming to the picture. The unsupervised approaches for extractive summarization again taking the popularity with the use of MMR technique by [Kurmi & Jain (2014)] which help reduce the redundancy in the summary. In 2016, [Jafari et al (2016)] used the combination of semantic analysis, feature-based approach and the fuzzy logic to improve the summaries.…”