# Abstractive Sentence Compression with Event Attention
## 5 Experiments
### Experimental Results  
Table 3 shows the sentence compression performance on MSR dataset. In this table,'seq-to-seq' and 'PG' denote the sequence-to-sequence model and the pointer generator, respectively. The model of [PERSON] et al. is based on the sequence-to-sequence with a deletion decoder and a copy-generator \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **MSR** & **Filippova** & **Korean** \\ \hline No. of training examples & 21,145 & 8000 & 2493 \\ No. of validation examples & 1908 & 1000 & 312 \\ No. of test examples & 3370 & 1000 & 312 \\ Average length of source sentences & 31.85 & 23.04 & 14.12 \\ Average length of target sentences & 21.97 & 9.34 & 8.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simple statistics on the datasets used. decoder [15]. Unlike the proposed model, it first conducts the deletion of a source sentence using the deletion decoder and then either generates words or copies the source sentence through the copy-generator decoder. According to the table, the use of event attention improves its base model. That is,'seq-to-seq + Event' shows higher performance than'seq-to-seq' in ROUGE metrics, and 'PG + Event' outperforms 'PG' in all metrics. In particular, 'PG + Event' achieves the best performance. On the other hand, the performance of [PERSON]'s model is lower than 'PG + Event', even if it is also based on the copy mechanism. This is because the errors by the deletion decoder are easy to propagate to the copy-generation decoder in [PERSON]'s model. The compression ratios denoted as **CR** are also given in this table to see how much a method compresses source sentences. The value within parentheses denotes the difference between the compression ratio of a method and the golden ratio. As a result, the smaller the absolute value of the difference is, the better a method compresses source sentences. The golden compression ratio on MSR dataset is 68.60%, and the difference between it and the compression of ratio of 'PG + Event' is smallest as \(-\)0.63. This result implies that the proposed model is an effective compressor as well as a good writer. Table 4 shows the results of sentence compression on Filippova dataset. When the event attention is applied to a baseline model, the performance of the baseline model improves. That is,'seq-to-seq + Event' is better than'seq-to-seq' and 'PG + Event' outperforms 'PG' for all evaluation metrics. The proposed model works effectively even with the dataset for deletion-based compression. However, in compression ratio, the proposed model is only slightly worse than 'PG'. The golden compression ratio of [PERSON] dataset is 42.62%, but the compression ratio of 'PG' is 42.45% while that of 'PG + Event' is 41.70%. The main reason 'PG' is better than 'PG + Event' in compression ratio is that this dataset is designed for deletion-based compression. To verify that the proposed model works for morphologically complex languages, we conducted sentence compression on Korean dataset. Table 5 shows the result on Korean sentence compression. Overall, the proposed model outperforms all baselines. That is, 'PG + Event' shows the best performance for all metrics. One thing to note is that the performance difference between 'PG + Event' and'seq-to-seq + Event' is large. This is because Korean dataset is relatively small, but its vocabulary size is large. Under this circumstance, the copy mechanism is much helpful in solving the out-of-vocabulary problem. In compression ratio, both 'PG' and 'PG + Event' are relatively closer to the golden compression ratio of Korean dataset. Please note that the overall performance of the proposed model for this dataset is lower than those for other datasets. This is because it is difficult