Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016)
0.01
0.0057247113 = product of:
0.02671532 = sum of:
0.0050448296 = weight(_text_:information in 2640) [ClassicSimilarity], result of:
0.0050448296 = score(doc=2640,freq=2.0), product of:
0.052020688 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.029633347 = queryNorm
0.09697737 = fieldWeight in 2640, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=2640)
0.014978974 = weight(_text_:retrieval in 2640) [ClassicSimilarity], result of:
0.014978974 = score(doc=2640,freq=2.0), product of:
0.08963835 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.029633347 = queryNorm
0.16710453 = fieldWeight in 2640, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=2640)
0.0066915164 = product of:
0.020074548 = sum of:
0.020074548 = weight(_text_:22 in 2640) [ClassicSimilarity], result of:
0.020074548 = score(doc=2640,freq=2.0), product of:
0.103770934 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.029633347 = queryNorm
0.19345059 = fieldWeight in 2640, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=2640)
0.33333334 = coord(1/3)
0.21428572 = coord(3/14)
- Abstract
- We propose a tag-based framework that simulates human abstractors' ability to select significant sentences based on key concepts in a sentence as well as the semantic relations between key concepts to create generic summaries of transcribed lecture videos. The proposed extractive summarization method uses tags (viewer- and author-assigned terms) as key concepts. Our method employs Flickr tag clusters and WordNet synonyms to expand tags and detect the semantic relations between tags. This method helps select sentences that have a greater number of semantically related key concepts. To investigate the effectiveness and uniqueness of the proposed method, we compare it with an existing technique, latent semantic analysis (LSA), using intrinsic and extrinsic evaluations. The results of intrinsic evaluation show that the tag-based method is as or more effective than the LSA method. We also observe that in the extrinsic evaluation, the grand mean accuracy score of the tag-based method is higher than that of the LSA method, with a statistically significant difference. Elaborating on our results, we discuss the theoretical and practical implications of our findings for speech video summarization and retrieval.
- Date
- 22. 1.2016 12:29:41
- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.2, S.366-379
