In recent times, data is growing rapidly in every domain such as news, social media,
banking, education, etc. Due to the excessiveness of data, there is a need of automatic …

This paper explores several unsupervised approaches to automatic keyword extraction
using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) …

This paper presents a supervised framework for extracting keywords from meeting
transcripts, a genre that is significantly different from written text or other speech domains …

Summarization is the process of reducing a text document to create a summary that retains
the most important points of the original document. Extractive summarizers work on the …

In this paper, we tackle the problem of automatic keyword extraction in the meeting domain,
a genre significantly different from written text. For the supervised framework, we proposed a …

Rooted in multi-document summarization, maximum marginal relevance (MMR) is a widely
used algorithm for meeting summarization (MS). A major problem in extractive MS using …

Keyphrases are essential meta-data that summarize the contents of an instructional video. In
this paper, we present a domain independent, statistical approach for automatic keyphrase …

Many studies show that online buzz influences consumer's behavior. Relatively few studies
empirically explore its diffusion, however. The source and content of online information …

In this paper, we present a multi-featured supervised automatic keyword extraction system.
We extracted salient semantic features which are descriptive of candidate keyphrases, a …

Keyphrases are set of words that reflect the main topic of interest of a document. It plays vital
roles in document summarization, text mining, and retrieval of web contents. As it is closely …