With the rapid development of the Internet, the massive amount of web textual data has
grown exponentially, which has brought considerable challenges to downstream tasks, such …

Currently used metrics for assessing summarization algorithms do not account for whether
summaries are factually consistent with source documents. We propose a weakly …

Text summarization aims at compressing long documents into a shorter form that conveys
the most important parts of the original document. Despite increased interest in the …

Graphs that capture relations between textual units have great benefits for detecting salient
information from multiple documents and generating overall coherent summaries. In this …

Abstractive text summarization is a task in natural language processing that automatically
generates the summary from the source document in a human-written form with minimal loss …

Neural controllable text generation is an important area gaining attention due to its plethora
of applications. Although there is a large body of prior work in controllable text generation …

High quality data forms the bedrock for building meaningful statistical models in NLP.
Consequently, data quality must be evaluated either during dataset construction or* post …

Abstractive summarization for long-document or multi-document remains challenging for the
Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text …

Comprehensive document encoding and salient information selection are two major
difficulties for generating summaries with adequate salient information. To tackle the above …

Abstract Entities and their mentions convey significant semantic information in documents. In
multidocument summarization, the same entity may appear across different documents …