Selecting the “right” amount of information to include in a summary is a...
Two-step approaches, in which summary candidates are generated-then-rera...
Language models (LMs) are increasingly being used in open-ended contexts...
Large language models (LLMs) have shown promise for automatic summarizat...
Recent work has identified noisy and misannotated data as a core cause o...
Many real-world applications of language models (LMs), such as code
auto...
Language models (LMs) are becoming the foundation for almost all major
l...
This paper introduces the shared task of summarizing documents in severa...
Summarizing novel chapters is a difficult task due to the input length a...
Machine learning models are now able to convert user-written text
descri...
Hate speech detection is complex; it relies on commonsense reasoning,
kn...
Model-based, reference-free evaluation metrics have been proposed as a f...
Despite recent progress in abstractive summarization, systems still suff...
Typical ASR systems segment the input audio into utterances using purely...
We introduce GEM, a living benchmark for natural language Generation (NL...
Leveraging large amounts of unlabeled data using Transformer-like
archit...
Users of machine translation (MT) may want to ensure the use of specific...
We introduce WikiLingua, a large-scale, multilingual dataset for the
eva...
We present a new summarization task, generating summaries of novel chapt...
Research in the social sciences and psychology has shown that the
persua...
Systems for automatic argument generation and debate require the ability...
We incorporate an explicit neural interlingua into a multilingual
encode...