A Hierarchical Decoder with Three-level Hierarchical Attention to Generate Abstractive Summaries of Interleaved Texts
Table 2: Summarization performance (Rouge Recall-Scores) comparing models when the threads are disentangled (top blue dotted section, upper bounds) and when the threads are entangled (bottom green dashed section, real-world) on the Easy, Medium and Hard Pubmed Corpora. ind = individual, dis = disentangled (ground-truth), kmn = K-means disentangled and ent = entangled. In the middle, the first row shows a seq2seq model trained on ground-truth disentangled texts and tested on unsupervised disentangled texts, and the second row shows a seq2seq model trained and tested on unsupervised disentangled texts. The best performance for the entangled threads and for the disentangled threads are in bold.
['Input Text', 'Model', '[BOLD] Easy Rouge-1', '[BOLD] Easy Rouge-2', '[BOLD] Easy Rouge-L', '[BOLD] Medium Rouge-1', '[BOLD] Medium Rouge-2', '[BOLD] Medium Rouge-L', '[BOLD] Hard Rouge-1', '[BOLD] Hard Rouge-2', '[BOLD] Hard Rouge-L']
The seq2seq model can use ground-truth disentanglement information in two ways, i.e., summarize threads individually or summarize concatenated threads. Clearly, seq2seq model can easily detect thread boundary in concatenated threads and perform as good as individual model.