Text Summarization using Abstract Meaning Representation

TL;DR

This paper introduces a novel text summarization pipeline utilizing Abstract Meaning Representation (AMR) to generate summaries, achieving state-of-the-art results and highlighting evaluation challenges.

Contribution

It presents a full pipeline for text summarization using AMR, including graph extraction and summary generation, with improved performance over existing AMR-based methods.

Findings

Achieved state-of-the-art results in AMR-based summarization

Identified significant issues in current evaluation methods

Demonstrated the effectiveness of the AMR pipeline

Abstract

With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding. In this work we explore a novel full-fledged pipeline for text summarization with an intermediate step of Abstract Meaning Representation (AMR). The pipeline proposed by us first generates an AMR graph of an input story, through which it extracts a summary graph and finally, generate summary sentences from this summary graph. Our proposed method achieves state-of-the-art results compared to the other text summarization routines based on AMR. We also point out some significant problems in the existing evaluation methods, which make them unsuitable for evaluating summary quality.