CADS: A Systematic Literature Review on the Challenges of Abstractive Dialogue Summarization
Frederic Kirstein, Jan Philip Wahle, Bela Gipp, Terry Ruas

TL;DR
This systematic review analyzes the challenges, techniques, datasets, and evaluation methods in Transformer-based abstractive dialogue summarization, highlighting progress in language handling but ongoing difficulties in comprehension, factuality, and salience.
Contribution
It provides a comprehensive taxonomy of challenges and links them to current techniques, datasets, and evaluation practices in dialogue summarization research.
Findings
Language challenges have seen progress due to training methods.
Comprehension, factuality, and salience remain difficult areas.
ROUGE is the most used evaluation metric, with limited human evaluation details.
Abstract
Abstractive dialogue summarization is the task of distilling conversations into informative and concise summaries. Although reviews have been conducted on this topic, there is a lack of comprehensive work detailing the challenges of dialogue summarization, unifying the differing understanding of the task, and aligning proposed techniques, datasets, and evaluation metrics with the challenges. This article summarizes the research on Transformer-based abstractive summarization for English dialogues by systematically reviewing 1262 unique research papers published between 2019 and 2024, relying on the Semantic Scholar and DBLP databases. We cover the main challenges present in dialog summarization (i.e., language, structure, comprehension, speaker, salience, and factuality) and link them to corresponding techniques such as graph-based approaches, additional training tasks, and planning…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
