Long documents such as academic articles and business reports have been the standard
format to detail out important issues and complicated subjects that require extra attention. An …

The recent success of zero-and few-shot prompting with models like GPT-3 has led to a
paradigm shift in NLP research. In this paper, we study its impact on text summarization …

A major challenge for scaling machine learning is training models to perform tasks that are
very difficult or time-consuming for humans to evaluate. We present progress on this …

Question answering (QA) is a fundamental means to facilitate assessment and training of
narrative comprehension skills for both machines and young children, yet there is scarcity of …

While human evaluation remains best practice for accurately judging the faithfulness of
automatically-generated summaries, few solutions exist to address the increased difficulty …

Text summarization helps readers capture salient information from documents, news,
interviews, and meetings. However, most state-of-the-art pretrained language models (LM) …

Summarizing book-length documents (> 100K tokens) that exceed the context window size
of large language models (LLMs) requires first breaking the input document into smaller …

Abstract We evaluate recent Large Language Models (LLMs) on the challenging task of
summarizing short stories, which can be lengthy, and include nuanced subtext or scrambled …

With the advent of large language models, methods for abstractive summarization have
made great strides, creating potential for use in applications to aid knowledge workers …

The ability to convey relevant and faithful information is critical for many tasks in conditional
generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal …