Text Summarization based on Semantic Graph

Semantic Summarization
Automatic text summarization is a main research area of natural language processing. The capability of consuming a large amount of documents and producing a concise textual summary is a manifestation of artificial intelligence. Most existing research focuses on extractive approaches, where important sentences are identified, optionally compressed, and concatenated to form a text summary. This project seeks to leverage Abstract Meaning Representation, a graph-based semantic representation of natural language, to develop text summarization approaches. Specifically, the input documents are merged into a source semantic graph, from which one or more summary graphs that carry important semantic meaning are identified. Natural language sentences are then generated from the summary (target) graphs to form an abstractive summary. Publications: NAACL 2015