Text Summarization using Abstract Meaning Representation
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.
Code (2)
Tasks
Abstract Meaning RepresentationNatural Language UnderstandingText SummarizationSimilar Papers 제목 키워드 기반
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation
Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and i…
Abstractive Text SummarizationAbstract Meaning RepresentationDecoderText GenerationToward Abstractive Summarization Using Semantic Representations
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs…
Abstractive Text SummarizationAbstract Meaning RepresentationAbstract Meaning Representation for Multi-Document Summarization
Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. Thi…
Abstract Meaning RepresentationDocument SummarizationMulti-Document SummarizationNeural Abstractive Text Summarizer for Telugu Language
Abstractive Text Summarization is the process of constructing semantically relevant shorter sentences which captures the essence of the overall meaning of the source text. It is actually difficult and very time consuming…
Abstractive Text SummarizationDecoderDeep LearningSentence+1Sequence Level Contrastive Learning for Text Summarization
Contrastive learning models have achieved great success in unsupervised visual representation learning, which maximize the similarities between feature representations of different views of the same image, while minimize…
Abstractive Text SummarizationContrastive LearningRepresentation LearningText Generation+1