A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization

Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M. Khapra, Shreyas Shetty


Abstract
Structured data summarization involves generation of natural language summaries from structured input data. In this work, we consider summarizing structured data occurring in the form of tables as they are prevalent across a wide variety of domains. We formulate the standard table summarization problem, which deals with tables conforming to a single predefined schema. To this end, we propose a mixed hierarchical attention based encoder-decoder model which is able to leverage the structure in addition to the content of the tables. Our experiments on the publicly available weathergov dataset show around 18 BLEU (around 30%) improvement over the current state-of-the-art.
Anthology ID:
N18-2098
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
622–627
Language:
URL:
https://aclanthology.org/N18-2098
DOI:
10.18653/v1/N18-2098
Bibkey:
Cite (ACL):
Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M. Khapra, and Shreyas Shetty. 2018. A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 622–627, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization (Jain et al., NAACL 2018)
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PDF:
https://aclanthology.org/N18-2098.pdf