曝光臺 注意防騙 網曝天貓店富美金盛家居專營店坑蒙拐騙欺詐消費者

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comparison with update inputs only avg. comparisons with update and background
features pyramid score responsiveness pyramid score responsiveness
JSD divergence -0.827 -0.764 -0.716 -0.669
JSD divergence smoothed -0.825 -0.764 -0.713 -0.670
% of ip topic wds in summ 0.770 0.709 0.677 0.616
KL divergence summ-inp -0.749 -0.709 -0.651 -0.624
KL divergence inp-summ -0.741 -0.717 -0.644 -0.638
cosine inp-summ 0.727 0.691 0.649 0.631
% of summary = topic wd 0.721 0.707 0.647 0.636
topic overlap inp- summ 0.707 0.674 0.645 0.619
multinomial summ prob 0.284 0.355 0.152 0.224
unigram summ prob -0.093 0.038 -0.151 -0.053
regression 0.789 0.605 0.699 0.522
regression combining features comparing with background and update inputs (without averaging)
correlations = 0.8058 with pyramid 1, 0.6729 with responsiveness
pyramid score responsiveness
features max min sig %sig a0.5 %a0.5 max min sig %sig a0.5 %a0.5
JSD smoothed -0.753 -0.269 41 85.4 23 47.9 -0.747 -0.266 36 75.0 16 33.3
JSD -0.746 -0.291 41 85.4 22 45.8 -0.738 -0.263 36 75.0 16 33.3
KL summ-inp -0.739 -0.293 41 85.4 20 41.7 -0.705 -0.275 37 77.1 15 31.3
% of sign from inp 0.778 0.277 38 79.2 17 35.4 0.706 0.297 29 60.4 13 27.1
cosine overlap 0.665 0.275 33 68.8 10 20.8 0.685 0.267 28 14.6 7 14.6
% summ sign terms 0.737 0.263 32 66.7 11 22.9 0.672 0.265 28 58.3 6 12.5
topic overlap 0.679 0.264 31 64.6 9 18.8 0.665 0.274 26 54.2 5 10.4
KL inp-summ -0.663 -0.281 30 62.5 9 18.8 -0.600 -0.285 24 50.0 5 10.4
mult. summ prob 0.479 0.267 12 25.0 0 0.0 0.547 0.262 13 27.1 1 2.1
uni. summ prob 0.363 0.362 1 2.1 0 0.0 0.266 0.266 1 2.1 0 0.0
regression 0.765 0.284 40 83.3 19 39.6 0.659 0.285 29 60.4 10 20.8
ROUGE-1 recall 0.842 0.392 48 100 41 85.4 0.811 0.268 46 95.8 30 62.5
ROUGE-2 recall 0.913 0.355 47 97.9 39 81.3 0.816 0.286 47 97.9 28 58.3
Table 4: Spearman correlations between fully automatic evaluation and manually assigned system scores for update summarization.
Results are reported separately for features comparing update summaries with the update input only or with both update and
background inputs and averaging the two (macro level). At the per-input level, only results for features comparing with update
inputs are reported.
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