@InProceedings{dohare-gupta-karnick:2018:ACL2018-SRW,
  author    = {Dohare, Shibhansh  and  Gupta, Vivek  and  Karnick, Harish},
  title     = {Unsupervised Semantic Abstractive Summarization},
  booktitle = {Proceedings of ACL 2018, Student Research Workshop},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {74--83},
  abstract  = {Automatic abstractive summary generation remains a significant open problem for natural language processing. In this work, we develop a novel pipeline for Semantic Abstractive Summarization (SAS). SAS, as introduced by Liu et. al. (2015) first generates an AMR graph of an input story, through which it extracts a summary graph and finally, creates summary sentences from this summary graph. Compared to earlier approaches, we develop a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes. Which we then use in a novel unsupervised algorithm based on how humans summarize a piece of text to extract the summary sub-graph. Our algorithm outperforms the state of the art SAS method by 1.7% F1 score in node prediction.},
  url       = {http://www.aclweb.org/anthology/P18-3011}
}