As large language models continue to advance in Artificial Intelligence (AI), text generation
systems have been shown to suffer from a problematic phenomenon often termed as" …

Specifically focusing on the landscape of abstractive text summarization, as opposed to
extractive techniques, this survey presents a comprehensive overview, delving into state-of …

Pre-trained language models (PLMs) have achieved outstanding achievements in
abstractive single-document summarization (SDS). However, such benefits may not fully …

A notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of
the input. In this paper, we present an extract-then-abstract Transformer framework to …

The problem of query-focused multi-document summarization (QMDS) is to generate a
summary from multiple source documents on identical/similar topics based on the query …

The availability of large-scale datasets has driven the development of neural models that
create generic summaries for single or multiple documents. For query-focused …

Research in Multi-document Summarization (MDS) mostly focuses on the English language
and depends on large MDS datasets that are not available for other languages. Some of …

Automatic summarization is attracting increasing attention as one of the most promising
research areas. This technology has been tried in various real-world applications in recent …

Deep neural networks have made tremendous gains in emulating human-like intelligence,
and have been used increasingly as ways of understanding how the brain may solve the …

In this paper, we propose an enhanced version of the vanilla transformer for data-to-text
generation and then use it as the generator of a conditional generative adversarial model to …