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 …

Inspired by how humans summarize long documents, we propose an accurate and fast
summarization model that first selects salient sentences and then rewrites them abstractively …

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 …

Recently BERT has been adopted for document encoding in state-of-the-art text
summarization models. However, sentence-based extractive models often result in …

Single document summarization has enjoyed renewed interests in recent years thanks to the
popularity of neural network models and the availability of large-scale datasets. In this paper …

Discourse parsing is an important research area in natural language processing (NLP),
which aims to parse the discourse structure of coherent sentences. In this survey, we …

Recent neural network approaches to summarization are largely either selection-based
extraction or generation-based abstraction. In this work, we present a neural model for …

Enormous amount of online information, available in legal domain, has made legal text
processing an important area of research. In this paper, we attempt to survey different text …

In this paper, we focus on learning structure-aware document representations from data
without recourse to a discourse parser or additional annotations. Drawing inspiration from …

Discourse structure is the hidden link between surface features and document-level
properties, such as sentiment polarity. We show that the discourse analyses produced by …