@InProceedings{jain-EtAl:2018:N18-2,
  author    = {Jain, Parag  and  Laha, Anirban  and  Sankaranarayanan, Karthik  and  Nema, Preksha  and  Khapra, Mitesh M.  and  Shetty, Shreyas},
  title     = {A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization},
  booktitle = {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},
  publisher = {Association for Computational Linguistics},
  pages     = {622--627},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-2098}
}