Text summarization is the process of condensing a long text into a shorter version by
maintaining the key information and its meaning. Automatic text summarization can save …

Abstractive text summarization is the task of compressing and rewriting a long document into
a short summary while maintaining saliency, directed logical entailment, and non …

Despite recent improvements in abstractive summarization, most current approaches
generate summaries that are not factually consistent with the source document, severely …

An accurate abstractive summary of a document should contain all its salient information
and should be logically entailed by the input document. We improve these important aspects …

Over the years as the technology advanced, the amount of data generated during the
simulations and processing has been constantly increasing. Techniques for creating …

Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic
representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR …

A system that could reliably identify and sum up the most important points of a conversation
would be valuable in a wide variety of real-world contexts, from business meetings to …

The size of data on the Internet has risen in an exponential manner over the past decade.
Thus, the need for a solution emerges, that transforms this vast raw information into useful …

Most earlier work on text summarization is carried out on news article datasets. The
summary in these datasets is naturally located at the beginning of the text. Hence, a model …

Knowledge retrieval and reasoning are two key stages in multi-hop question answering (QA)
at web scale. Existing approaches suffer from low confidence when retrieving evidence facts …