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A Semantically Motivated Approach to Compute ROUGE Scores | | Elaheh ShafieiBavani
; Mohammad Ebrahimi
; Raymond Wong
; Fang Chen
; | | Date: |
20 Oct 2017 | | Abstract: | ROUGE is one of the first and most widely used evaluation metrics for text
summarization. However, its assessment merely relies on surface similarities
between peer and model summaries. Consequently, ROUGE is unable to fairly
evaluate abstractive summaries including lexical variations and paraphrasing.
Exploring the effectiveness of lexical resource-based models to address this
issue, we adopt a graph-based algorithm into ROUGE to capture the semantic
similarities between peer and model summaries. Our semantically motivated
approach computes ROUGE scores based on both lexical and semantic similarities.
Experiment results over TAC AESOP datasets indicate that exploiting the
lexico-semantic similarity of the words used in summaries would significantly
help ROUGE correlate better with human judgments. | | Source: | arXiv, 1710.7441 | | Services: | Forum | Review | PDF | Favorites | |
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