TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records

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.