Sentence Compression in Spanish driven by Discourse Segmentation and Language Models
Table 1: Gramaticallity tests.
['Algorithm', 'Avgerage CR', 'Coherence', 'Agrammatical compressions']
The results confirm that scoring compressions before producing a summary improves the text quality in summaries with compressed sentences. It is very surprising that, for Human2, the number of compressions judged grammatically incorrect is greater than that of our systems. May be Human2 misunderstood the compression instructions. However, considering as limits the results of Human1 and Random baseline system we consider that the proportion of bad formed sentences is very low. We confirmed our initial intuition that preserving the first segment tends to save the main subject in most of the cases. The introduction of this simple heuristic in the First system improves the grammar quality of productions. We wanted to evaluate the content quality of summaries with the ROUGE package [lin:2003]. ROUGE is used to evaluate summaries because some results show that it correlates well with human judgements [lin:2004rpa]. We assumed that Random system would have worst results with respect to First system. Other than that, again, First system overcomes All system. The FRESA score FM asses the summaries qualities. Lower values of FM means significant difference whit respect to the original text (i.e. more radical compressions). Considering FM values, we see that First system is found more related to Human1 than Human2 by FRESA and Human2 is closer to Random system performance. The FM value of All system suggests that it is the most aggressive approach.