We present an approach for extractive single-document summarization. Our approach is
based on a weighted graphical representation of documents obtained by topic modeling. We …

As social media has become more prevalent, its influence on business, politics, and society
has become significant. Due to easy access and interaction between large numbers of …

Automatic text summarization plays a vital role in text retrieval. However, very little interest,
as well as attention to Hindi text summarization and the wide use of hybridization of an …

In today's scenario the rate of growth of information is expanding exponentially in the World
Wide Web. As a result, extracting valid and useful information from a huge data has become …

Abstract Today, World Wide Web has brought us enormous quantity of on-line information.
As a result, extracting relevant information from massive data has become a challenging …

Large collections of videos are grouped into clusters by a topic keyword, such as" Eiffel
Tower" or" Surfing", with many important visual concepts repeating across them. Such a …

We propose a graph-based method for extractive single-document summarization which
considers importance, non-redundancy and local coherence simultaneously. We represent …

Previous work on automatic summarization does not thoroughly consider coherence while
generating the summary. We introduce a graph-based approach to summarize scientific …

The semantic link network is a semantics modeling method for effective information services.
This paper proposes a new text summarization approach that extracts semantic link network …

Extracting keyphrases from documents automatically is an important and interesting task
since keyphrases provide a quick summarization for documents. Although lots of efforts have …