Data visualization in the form of charts plays a pivotal role in data analysis, offering critical
insights and aiding in informed decision-making. Automatic chart understanding has …

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 …

Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge
bases and preventing hallucinations in sequence-to-sequence models. Drawing on humans' …

Previous research in multi-document news summarization has typically concentrated on
collating information that all sources agree upon. However, the summarization of diverse …

Generative AI models exhibit remarkable potential; however, hallucinations across various
tasks present a significant challenge, particularly for longer inputs that current approaches …

Query-focused summarization (QFS) is a fundamental task in natural language processing
with broad applications, including search engines and report generation. However …

Factual consistency is an important quality in dialogue summarization. Large language
model (LLM)-based automatic text summarization models generate more factually consistent …

Abstractive dialogue summarization requires distilling and rephrasing key information from
noisy multi-speaker documents. Combining pre-trained language models with input …

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 …

Abstractive dialogue summarization is the task of distilling conversations into informative
and concise summaries. Although focused reviews have been conducted on this topic, there …