Extraction of Salient Sentences fromLabelled Documents
Table 1: Results of classifying extracted documents with the reference model. Results labelled Proportion indicate selecting up to the indicated percentage of sentences in the review, and results labelled Fixed show the result of selecting a fixed number of sentences from each. ConvNet shows reference model performance on extractions produced by our ConvNet. Word2Vec shows reference performance on extractions produced by the shallow baseline. Rand shows reference performance on randomly created extractions. The final two rows show reference accuracy on the full reviews (Full) and when using the common heuristic of extracting the first and last sentence (First+Last).
['Proportion 50%', 'ConvNet  [BOLD] 82.74', 'Word2Vec 81.98', 'Rand. 79.79', 'Fixed Pick 5', 'ConvNet  [BOLD] 83.12', 'Word2Vec 82.26', 'Rand. 80.02']
Even keeping only two sentences from each review, the accuracy of the reference model drops by less than 1.5% on the test set. In all cases our convolutional model outperforms all of the baselines we compare against.