Papers › Multimodal Abstractive Summarization for How2 Videos
Multimodal Abstractive Summarization for How2 Videos
Shruti Palaskar, Jindrich Libovický, Spandana Gella, Florian Metze
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Summarization | How2 | Ground-truth transcript + Action with Hierarchical Attn | Content F1 | 48.9 | #1 of 2 | Archive leaderboard | report |
| Text Summarization | How2 | Ground-truth transcript + Action with Hierarchical Attn | ROUGE-L | 54.9 | #1 of 2 | Archive leaderboard | report |
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