Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP)
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …

This paper reviews the state-of-the-art of language models architectures and strategies for"
complex" question-answering (QA, CQA, CPS) with a focus on hybridization. Large …

While large language models (LLMs) already achieve strong performance on standard
generic summarization benchmarks, their performance on more complex summarization …

While large language models (LLMs) like ChatGPT have shown impressive capabilities in
Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this …

Query-focused summarization (QFS) is a challenging task in natural language processing
that generates summaries to address specific queries. The broader field of Generative …

Generic text summarization approaches often fail to address the specific intent and needs of
individual users. Recently, scholarly attention has turned to the development of …

The use of argumentation in education has been shown to improve critical thinking skills for
end-users such as students, and computational models for argumentation have been …

Reframing a negative into a positive thought is at the crux of several cognitive approaches to
mental health and psychotherapy that could be made more accessible by large language …

Query-Focused Meeting Summarization (QFMS) aims to generate a summary of a given
meeting transcript conditioned upon a query. The main challenges for QFMS are the long …

Dialog summarization has become increasingly important in managing and comprehending
large-scale conversations across various domains. This task presents unique challenges in …