On Improving Informativity and Grammaticality for Multi-Sentence Compression
Table 3: Average scores over normal and diverse clusters separately given by the raters; along with the estimated compression rate
['[BOLD] Method', '[BOLD] Normal  [BOLD] Info.', '[BOLD] Normal  [BOLD] Gram.', '[BOLD] Diverse  [BOLD] Info.', '[BOLD] Diverse  [BOLD] Gram.', '[BOLD] CompR']
Two existing approaches, i.e., Filippova (2010) and Boudin and Morin (2013) are used as Baseline1 and Baseline2 respectively, for comparison purposes in our experiments. To better understand the behavior of our system, we examined our test dataset, and made the following observations. The informativity of Baseline1 is adversely influenced by missing important information about the set of related sentences [boudin2013keyphrase]. However Baseline2 enhanced the informativity, the grammaticality scores are decreased due to the outputs of longer compressions. In our approach, the remarkable improvement in the grammaticality scores is due to the adding of the syntactic-based re-ranking step. Using this re-ranking method, the most grammatical sentences are picked among the k-best compression candidates. Furthermore, merging MWEs, replacing them with their available one-word synonyms and mapping words using synonymy all enhance the informativity scores, and help to generate a denser word graph instead of a sparse one. Given that, the value of the compression ratio (∼48%) is better than the best obtained compression ratio on these two baselines (50%).