Abstractive and Extractive Text Summarization
Table 7. Results on supervised task using human extracted summaries - 15K for training and 5K for evaluation. 2 variations with target summary: When target summaries are extremely long (5 sentence). Extractive Context RNN (EC-RNN) performs best. Adding context as a whole created improvements in RNN models.
['Model', 'Token', '[ITALIC] Rouge1', '[ITALIC] Rouge2', 'Rouge', 'BLEU', 'Topic']
For large target summaries which are 3 sentence long extractive contextual RNNs perform the best followed by abstractive contextual RNNs. For example rouge-1 for EC-RNN is 0.39 and for AC-RNN is 0.33. Similarly BLEU is 0.25 compared to 0.22 for AC-RNN. For small target summaries (1 sentence long) we observe that abstractive-context-rnn (AC-RNN) outperform extractive-context-rnn (EC-RNN). Where rouge-1 is 0.15 for EC-RNN compared to 0.19 for AC-RNN. As the target summary length increases efficacy of extractive contextual RNN increases over abstractive contextual RNNs.