Word sense disambiguation (WSD) is the ability to identify the meaning of words in context
in a computational manner. WSD is considered an AI-complete problem, that is, a task …

The abundant volume of natural language text in the connected world, though having a
large content of knowledge, but it is becoming increasingly difficult to disseminate it by a …

When I planned this book in 1999, it was intended to be a survey of current work in text
summarization. The survey would discuss various efforts in the context of a framework for …

Textual information in the form of digital documents quickly accumulates to create huge
amounts of data. The majority of these documents are unstructured: it is unrestricted text and …

With the advent of Web 2.0, there exist many online platforms that results in massive textual
data production such as social networks, online blogs, magazines etc. This textual data …

This paper proposes two approaches to address text summarization: modified corpus-based
approach (MCBA) and LSA-based TRM approach (LSA+ TRM). The first is a trainable …

Keywords can be considered as condensed versions of documents and short forms of their
summaries. In this paper, the problem of automatic extraction of keywords from documents is …

While automatic text summarization is an area that has received a great deal of attention in
recent research, the problem of efficiency in this task has not been frequently addressed …

In the Web 3.0 platforms, enormous amount of information is shared whereby individuals
express their thoughts and opinions and learn from others' experiences. Many e-commerce …

Document summarization and keyphrase extraction are two related tasks in the IR and NLP
fields, and both of them aim at extracting condensed representations from a single text …