Abstract
We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.- Anthology ID:
- D17-1101
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 966–971
- Language:
- URL:
- https://aclanthology.org/D17-1101
- DOI:
- 10.18653/v1/D17-1101
- Cite (ACL):
- Licheng Yu, Mohit Bansal, and Tamara Berg. 2017. Hierarchically-Attentive RNN for Album Summarization and Storytelling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 966–971, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Hierarchically-Attentive RNN for Album Summarization and Storytelling (Yu et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/D17-1101.pdf
- Data
- VIST