Text mining in big data analytics is emerging as a powerful tool for harnessing the power of
unstructured textual data by analyzing it to extract new knowledge and to identify significant …

While automatic keyphrase extraction has been examined extensively, state-of-theart
performance on this task is still much lower than that on many core natural language …

Due to the considerable growth of the volume of text documents on the Internet and in digital
libraries, manual analysis of these documents is no longer feasible. Having efficient …

In this paper, we address the keyphrase extraction problem as sequence labeling and
propose a model that jointly exploits the complementary strengths of Conditional Random …

This paper describes the organization and results of the automatic keyphrase extraction task
held at the Workshop on Semantic Evaluation 2010 (SemEval-2010). The keyphrase …

This paper develops a framework for facilitating organizational learning through social
media text analytics to enhance citizen-centric public service quality. Theoretically, the …

Transcripts of natural, multi-person meetings differ significantly from documents like news
articles, which can make Natural Language Generation models for generating summaries …

Due to the exponential growth of textual data and web sources, an automatic mechanism is
required to identify relevant information embedded within them. The utility of Automatic …

We study the problem of generating keyphrases that summarize the key points for a given
document. While sequence-to-sequence (seq2seq) models have achieved remarkable …

State-of-the-art approaches for unsupervised keyphrase extraction are typically evaluated
on a single dataset with a single parameter setting. Consequently, it is unclear how effective …