We introduce PRIMERA, a pre-trained model for multi-document representation with a focus
on summarization that reduces the need for dataset-specific architectures and large …

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved
remarkable successes over the last two years in a range of different applications. In spite of …

We present BLANC, a new approach to the automatic estimation of document summary
quality. Our goal is to measure the functional performance of a summary with an objective …

While online conversations can cover a vast amount of information in many different formats,
abstractive text summarization has primarily focused on modeling solely news articles. This …

In this paper, we formulate the personalized news headline generation problem whose goal
is to output a user-specific title based on both a user's reading interests and a candidate …

A large majority of American adults get at least some of their news from the Internet. Even
though many online news products have the goal of informing their users about the news …

While neural sequence learning methods have made significant progress in single-
document summarization (SDS), they produce unsatisfactory results on multi-document …

Automatic headline generation enables users to comprehend ongoing news events
promptly and has recently become an important task in web mining and natural language …

The integration of multi-document pre-training objectives into language models has resulted
in remarkable improvements in multi-document downstream tasks. In this work, we propose …

To explore how humans can best leverage LLMs for writing and how interacting with these
models affects feelings of ownership and trust in the writing process, we compared common …