Abstractive and Extractive Text Summarization
Table 6. Results on supervised task using human extracted summaries - 15K for training and 5K for evaluation. 2 variations with target summary :- (a) 1 sentence long and (b) 3 sentences long. Abstractive Context RNN (AC-RNN) performs best in all metrics for short summaries. Extractive Context RNN (EC-RNN) performs best for longer summaries. Adding context as a whole created improvements in RNN models.
['Model', 'Token', '[ITALIC] Rouge1', '[ITALIC] Rouge2', 'Rouge', 'BLEU', 'Topic']
It is clear that RNN’s as a whole outperform other techniques (Naive Bayes, SVM, LSA, LexRank, TextRank) on Rouge, BLEU and token similarity. In both abstractive and extractive RNN’s adding document-context improves all metrics. For example, Rouge-1 and BLEU for abstractive RNN is 0.27 and 0.19 respectively. When document context is added (AC-RNN) then Rouge-1 and BLEU increases to 0.33 and 0.22 respectively. Similarly for Extractive RNN (E-RNN) vs extractive contextual RNN (EC-RNN) rouge-1 changes from 0.27 to 0.39 and BLEU changes from 0.16 to 0.25.