The past few years have witnessed increased interest in the potential use of wireless sensor
networks (WSNs) in applications such as disaster management, combat field …

We present a multi-document summarizer, MEAD, which generates summaries using cluster
centroids produced by a topic detection and tracking system. We describe two new …

Numerous approaches for identifying important content for automatic text summarization
have been developed to date. Topic representation approaches first derive an intermediate …

It has now been 50 years since the publication of Luhn's seminal paper on automatic
summarization. During these years the practical need for automatic summarization has …

As the amount of on-line information increases, systems that can automatically summarize
one or more documents become increasingly desirable. Recent research has investigated …

Text summarization is compress the source text into a diminished version conserving its
information content and overall meaning. Because of the great amount of the information we …

We present an exploration of generative probabilistic models for multi-document
summarization. Beginning with a simple word frequency based model (Nenkova and …

When humans produce summaries of documents, they do not simply extract sentences and
concatenate them. Rather, they create new sentences that are grammatical, that cohere with …

In this paper, we propose a simple, fast decoding algorithm that fosters diversity in neural
generation. The algorithm modifies the standard beam search algorithm by adding an inter …

When humans produce summaries of documents, they do not simply extract sentences and
concatenate them. Rather, they create new sentences that are grammatical, that cohere with …