Abstract
In this paper we study the performance of several state-of-the-art sequence-to-sequence models applied to generation of short company descriptions. The models are evaluated on a newly created and publicly available company dataset that has been collected from Wikipedia. The dataset consists of around 51K company descriptions that can be used for both concept-to-text and text-to-text generation tasks. Automatic metrics and human evaluation scores computed on the generated company descriptions show promising results despite the difficulty of the task as the dataset (like most available datasets) has not been originally designed for machine learning. In addition, we perform correlation analysis between automatic metrics and human evaluations and show that certain automatic metrics are more correlated to human judgments.- Anthology ID:
- W18-6532
- Volume:
- Proceedings of the 11th International Conference on Natural Language Generation
- Month:
- November
- Year:
- 2018
- Address:
- Tilburg University, The Netherlands
- Editors:
- Emiel Krahmer, Albert Gatt, Martijn Goudbeek
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 254–263
- Language:
- URL:
- https://aclanthology.org/W18-6532
- DOI:
- 10.18653/v1/W18-6532
- Cite (ACL):
- Raheel Qader, Khoder Jneid, François Portet, and Cyril Labbé. 2018. Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation. In Proceedings of the 11th International Conference on Natural Language Generation, pages 254–263, Tilburg University, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation (Qader et al., INLG 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W18-6532.pdf