Despite significant progress in understanding and improving faithfulness in abstractive
summarization, the question of how decoding strategies affect faithfulness is less studied …

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 …

With the proliferation of social media platforms, users are exposed to vast information,
including posts containing misleading claims. However, the pervasive noise inherent in …

Recent research on explainable recommendation generally frames the task as a standard
text generation problem, and evaluates models simply based on the textual similarity …

Abstract Information extraction (IE) and summarization are closely related, both tasked with
presenting a subset of the information contained in a natural language text. However, while …

Abstract Multimodal Large Language Models (MLLMs) excel at synthesizing key information
from diverse sources. However, generating accurate and faithful multimodal summaries is …

Across all fields of academic study, experts cite their sources when sharing information.
While large language models (LLMs) excel at synthesizing information, they do not provide …

Knowledge-grounded dialogue generation is a challenging task because it requires
satisfying two fundamental yet often competing constraints: being responsive in a manner …

Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to
reduce reliance on human-labeled data and improve the performance of summarization …

In abstractive summarization, the challenge of producing concise and accurate summaries
arises from the vast amount of information contained in the source document. Consequently …