Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output
to biased and toxic generations. Due to several limiting factors surrounding LLMs (training …

Despite tremendous improvements in natural language generation, summarization models
still suffer from the unfaithfulness issue. Previous work evaluates faithfulness either using …

In the rapidly evolving landscape of medical research, accurate and concise summarization
of clinical studies is crucial to support evidence-based practice. This paper presents a novel …

In e-commerce, opinion summarization is the process of condensing the opinions presented
in product reviews. However, the absence of large amounts of supervised datasets presents …

Improving factual consistency of abstractive summarization has been a widely studied topic.
However, most of the prior works on training factuality-aware models have ignored the …

Abstractive summarization methods typically follow the autoregressive paradigm using the
causal masks in the decoder for training and inference efficiency. However, this approach …

Improving factual consistency in abstractive summarization has been a focus of current
research. One promising approach is the post-editing method. However, previous works …

Factual consistency problem has become an increasingly important issue in summarization
in recent years. Since 2019, researchers have continuously pointed out that there are many …