Automatic metrics are used as proxies to evaluate abstractive summarization systems when human annotations are too expensive.To be useful, these metrics should be fine-grained, show a high correlation with human annotations, and ideally be independent of reference quality; however, most standard evaluation metrics for summarization are reference-based, and existing reference-free metrics correlate poorly with relevance, especially on summaries of longer documents.In this paper, we introduce a reference-free metric that correlates well with human evaluated relevance, while being very cheap to compute.We show that this metric can also be used alongside reference-based metrics to improve their robustness in low quality reference settings.

Large language models and multimodal language-vision models give impressive results on current available summarization benchmarks, but are not designed to handle long multimodal documents. Most summarization datasets are composed of either mono-modal documents or short multimodal documents. In order to develop models designed for understanding and summarizing real-world videoconference records that are typically around 1 hour long, we propose a dataset of 9,103 videoconference records extracted from the German National Library of Science and Technology (TIB) archive, along with their abstract. Additionally, we process the content using automatic tools in order to provide the transcripts and key frames. Finally, we present experiments for abstractive summarization, to serve as baseline for future research work in multimodal approaches.

Training and evaluating language models increasingly requires the construction of meta-datasets -- diverse collections of curated data with clear provenance. Natural language prompting has recently lead to improved zero-shot generalization by transforming existing, supervised datasets into a variety of novel instruction tuning tasks, highlighting the benefits of meta-dataset curation. While successful in general-domain text, translating these data-centric approaches to biomedical language modeling remains challenging, as labeled biomedical datasets are significantly underrepresented in popular data hubs. To address this challenge, we introduce BigBio a community library of 126+ biomedical NLP datasets, currently covering 13 task categories and 10+ languages. BigBio facilitates reproducible meta-dataset curation via programmatic access to datasets and their metadata, and is compatible with current platforms for prompt engineering and end-to-end few/zero shot language model evaluation. We discuss our process for task schema harmonization, data auditing, contribution guidelines, and outline two illustrative use cases: zero-shot evaluation of biomedical prompts and large-scale, multi-task learning. BigBio is an ongoing community effort and is available at https://github.com/bigscience-workshop/biomedical