%0 Conference Proceedings %T Multimodal Abstractive Summarization for How2 Videos %A Palaskar, Shruti %A Libovický, Jindřich %A Gella, Spandana %A Metze, Florian %Y Korhonen, Anna %Y Traum, David %Y Màrquez, Lluís %S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics %D 2019 %8 July %I Association for Computational Linguistics %C Florence, Italy %F palaskar-etal-2019-multimodal %X In this paper, we study abstractive summarization for open-domain videos. Unlike the traditional text news summarization, the goal is less to “compress” text information but rather to provide a fluent textual summary of information that has been collected and fused from different source modalities, in our case video and audio transcripts (or text). We show how a multi-source sequence-to-sequence model with hierarchical attention can integrate information from different modalities into a coherent output, compare various models trained with different modalities and present pilot experiments on the How2 corpus of instructional videos. We also propose a new evaluation metric (Content F1) for abstractive summarization task that measures semantic adequacy rather than fluency of the summaries, which is covered by metrics like ROUGE and BLEU. %R 10.18653/v1/P19-1659 %U https://aclanthology.org/P19-1659 %U https://doi.org/10.18653/v1/P19-1659 %P 6587-6596