The task of automatic document summarization aims at generating short summaries for
originally long documents. A good summary should cover the most important information of …

As information is available in abundance for every topic on internet, condensing the
important information in the form of summary would benefit a number of users. Hence, there …

We present a novel abstractive summarization framework that draws on the recent
development of a treebank for the Abstract Meaning Representation (AMR). In this …

Summarization of legal case judgments is an important problem because the huge length
and complexity of such documents make them difficult to read as a whole. Many …

Summarization of legal case judgement documents is a practical and challenging problem,
for which many summarization algorithms of different varieties have been tried. In this work …

We present a discriminative model for single-document summarization that integrally
combines compression and anaphoricity constraints. Our model selects textual units to …

With the explosive growth of microblogging services, short-text messages (also known as
tweets) are being created and shared at an unprecedented rate. Tweets in its raw form can …

Research on summarization has mainly been driven by empirical approaches, crafting
systems to perform well on standard datasets with the notion of information Importance …

Despite tremendous progress in automatic summarization, state-of-the-art methods are
predominantly trained to excel in summarizing short newswire articles, or documents with …

We present a novel unsupervised query-focused multi-document summarization approach.
To this end, we generate a summary by extracting a subset of sentences using the Cross …