Text Summarization using Abstract Meaning Representation
This work addresses the problem of generating summaries from large text volumes for natural language understanding, but it is incremental as it builds on existing AMR-based approaches.
The authors tackled automatic text summarization by introducing a novel pipeline that uses Abstract Meaning Representation (AMR) as an intermediate step, achieving state-of-the-art results compared to other AMR-based methods.
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.