Experiments are reported that investigate the effect of various source document representations on the accuracy of the sentence extraction phase of a multidocument summarisation task. A novel representation is introduced based on generic relation extraction (GRE), which aims to build systems for relation identification and characterisation that can be transferred across domains and tasks without modification of model parameters. Results demonstrate performance that is significantly higher than a non-trivial baseline that uses tf*idf -weighted words and at least as good as a comparable but less general approach from the literature. Analysis shows that the representations compared are complementary, suggesting that extraction performance could be further improved through system combination.
REF investigates the effect of various source document representations on the accuracy of the sentence extraction phase of a multi-document summarization task.
Multi-Document Summarisation Using Generic Relation Extraction
EMNLP
Computer Science