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

Hallucinations pose a significant challenge to the reliability of neural models for abstractive
summarisation. While automatically generated summaries may be fluent, they often lack …

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 …

Although current summarization models can process increasingly long text sequences, they
still struggle to capture salient related information spread across the lengthy size of inputs …

Extractive summaries are usually presented as lists of sentences with no expected cohesion
between them and with plenty of redundant information if not accounted for. In this paper, we …

Neural knowledge-to-text generation models often struggle to faithfully generate
descriptions for the input facts: they may produce hallucinations that contradict the given …

Document structure plays a crucial role in understanding and analyzing document
information. However, effectively encoding document structural features into the Transformer …

Graphs provide a natural, intuitive, and holistic means to capture relationships between
different text elements in Natural Language Processing (NLP) such as words, sentences …

Abstractive multi-document summarization aims to generate a comprehensive summary that
encapsulates crucial content derived from multiple input documents. Despite the proficiency …

Document logical structuring aims to extract the underlying hierarchical structure of
documents, which is crucial for document intelligence. Traditional approaches often fall short …