Hobson, S.P.; Dorr, B.J.; Monz, C.; Schwartz, R.: Task-based evaluation of text summarization using Relevance Prediction (2007)
0.00
0.003658936 = product of:
0.007317872 = sum of:
0.007317872 = product of:
0.014635744 = sum of:
0.014635744 = weight(_text_:a in 938) [ClassicSimilarity], result of:
0.014635744 = score(doc=938,freq=26.0), product of:
0.053105544 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046056706 = queryNorm
0.27559727 = fieldWeight in 938, product of:
5.0990195 = tf(freq=26.0), with freq of:
26.0 = termFreq=26.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=938)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- This article introduces a new task-based evaluation measure called Relevance Prediction that is a more intuitive measure of an individual's performance on a real-world task than interannotator agreement. Relevance Prediction parallels what a user does in the real world task of browsing a set of documents using standard search tools, i.e., the user judges relevance based on a short summary and then that same user - not an independent user - decides whether to open (and judge) the corresponding document. This measure is shown to be a more reliable measure of task performance than LDC Agreement, a current gold-standard based measure used in the summarization evaluation community. Our goal is to provide a stable framework within which developers of new automatic measures may make stronger statistical statements about the effectiveness of their measures in predicting summary usefulness. We demonstrate - as a proof-of-concept methodology for automatic metric developers - that a current automatic evaluation measure has a better correlation with Relevance Prediction than with LDC Agreement and that the significance level for detected differences is higher for the former than for the latter.
- Type
- a