Improving Abstractive Text Summarization with History Aggregation
Table 3: We compare different layers of encoder(E) and decoder(D) and report results on CNN/DailyMail test dataset using precision/recall/F1 scores of ROUGE.
['E/D 4/4', 'ROUGE-1(P/R/F1) 40.46', 'ROUGE-1(P/R/F1) 41.53', 'ROUGE-1(P/R/F1) 40.05', 'ROUGE-2(P/R/F1) 18.11', 'ROUGE-2(P/R/F1) 18.42', 'ROUGE-2(P/R/F1) 17.72', 'ROUGE-l(P/R/F1) 36.42', 'ROUGE-l(P/R/F1) 37.15', 'ROUGE-l(P/R/F1) 36.77']
It can be concluded from the comparison of each model results that we can get lower precision but higher recall score when the encoder layers are decreasing and we have opposite results on the decoder layers decreasing experiments. Meanwhile, we can get a higher ROUGE-1 F1 score and lower ROUGE-2, L F1 scores in the model decreasing each 1 decoder layer compared to that decreasing each 1 encoder layer. Therefore, we can conclude that the encoder captures the features of the source text while the decoder makes summaries consistently.