  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.
