Untitled

Abstract

Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures. While such methods model nodes' local pairwise importance, they lack the capability to model global importance relative to other nodes of the graph. This causes such models to miss critical information in tasks where global ranking is a key component for the task, such as in keyphrase extraction. We address this shortcoming by allowing the proper incorporation of global information into the GCN family of models through the use of scaled node weights. In the context of keyphrase extraction, incorporating global random walk scores obtained from Tex-tRank boosts performance significantly. With our proposed method, we achieve state-of-theart results, bettering a strong baseline by an absolute 2% increase in F 1 score.

Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization

“…Previously proposed models include the RNN-based sequence model (Nallapati et al, 2017), the attention-based neural encoder-decoder model (Cheng and Lapata, 2016), and the sequence model with a global learning objective (e.g., ROUGE) for ranking sentences optimized via RL (Narayan et al, 2018;Paulus et al, 2018). More recently, graph convolutional neural networks (GCNs) have also been adapted to allow the incorporation of global information in text summarization tasks (Sun et al, 2019;Prasad and Kan, 2019). Abstractive summarization is typically cast as a sequence-to-sequence learning problem.…”