Abstract
We introduce an approach based on using the dependency grammar representations of sentences to compute sentence similarity for extractive multi-document summarization. We adapt and investigate the effects of two untyped dependency tree kernels, which have originally been proposed for relation extraction, to the multi-document summarization problem. In addition, we propose a series of novel dependency grammar based kernels to better represent the syntactic and semantic similarities among the sentences. The proposed methods incorporate the type information of the dependency relations for sentence similarity calculation. To our knowledge, this is the first study that investigates using dependency tree based sentence similarity for multi-document summarization.- Anthology ID:
- L16-1452
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 2833–2838
- Language:
- URL:
- https://aclanthology.org/L16-1452
- DOI:
- Cite (ACL):
- Şaziye Betül Özateş, Arzucan Özgür, and Dragomir Radev. 2016. Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2833–2838, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization (Özateş et al., LREC 2016)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/L16-1452.pdf