Language as a Latent Variable: Discrete Generative Models for Sentence Compression
Table 2: Abstractive Summarisation Performance. The abstractive summaries of these models are decoded by the combined pointer network (i.e. the shared pointer network together with the softmax output layer over the full vocabulary).
['Model', 'Training Data Labelled', 'Training Data Unlabelled', 'Recall R-1', 'Recall R-2', 'Recall R-L', 'Precision R-1', 'Precision R-2', 'Precision R-L', 'F-1 R-1', 'F-1 R-2', 'F-1 R-L']
The second set of experiments evaluate performance on abstractive summarisation Consistently, we see that adding the generative objective to the discriminative model (ASC+FSC1) results in a significant boost on all the Rouge scores, while employing extra unlabelled data increase performance further (ASC+FSC2). This validates the effectiveness of transferring the knowledge learned on unlabelled data to the supervised abstractive summarisation.