Unsupervised Semantic Abstractive Summarization
Shibhansh Dohare, Vivek Gupta, Harish Karnick · 2018
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-ofthe-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.