A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
Table 2: ROUGE performance of various methods on the test set.
['Method', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
ROUGE measures the summary quality by counting the overlapping units such as n-grams between the generated summary and reference summaries. We consider ROUGE-1 (uni-grams), ROUGE-2 (bi-grams) and ROUGE-L (longest-common subsequence) as our automatic evaluation metrics. As expected, the truncation-based method achieves the worst ROUGE scores because of its ignorance of semantics. The main reason is that core product words are usually appear in the latter part of the titles, and truncation-based methods tend to miss some of them in the compressed titles by taking only the lead.