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 …

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 …

Text summarization solves the problem of presenting the information needed by a user in a
compact form. There are different approaches to creating well-formed summaries. One of the …

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

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 …

We study the problem of generating a summary for a given sentence. Existing researches on
abstractive sentence summarization ignore that keywords in the input sentence provide …

We present a new neural sequence-to-sequence model for extractive summarization called
SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive …

We introduce a supervised model for predicting word importance that incorporates a rich set
of features. Our model is superior to prior approaches for identifying words used in human …

Previous methods usually conduct the keyphrase extraction task for single documents
separately without interactions for each document, under the assumption that the documents …