# Abstractive Sentence Compression with Event Attention
## 5 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **BLEU** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **CR** (\%) \\ \hline seq-to-seq & 25.62 & 53.14 & 36.12 & 50.59 & 38.61 (\(-\)4.01) \\ seq-to-seq + Event & 28.70 & 56.03 & 39.77 & 53.51 & 38.72 (\(-\)3.90) \\ PG & 42.50 & 68.60 & 57.06 & 65.83 & **42.45** (\(-\)0.17) \\ PG + Event & **46.28** & **72.54** & **58.97** & **68.94** & 41.70 (\(-\)0.92) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results on [PERSON] dataset. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **BLEU** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **CR**(\%) \\ \hline seq-to-seq & 18.32 & 30.05 & 10.42 & 26.87 & 69.39 (+0.79) \\ seq-to-seq + Event & 18.04 & 45.70 & 25.94 & 41.63 & 59.03 (\(-\)9.57) \\ Yu et al. & 26.30 & 36.21 & 17.43 & 33.72 & 65.53 (\(-\)3.07) \\ PG & 31.70 & 61.35 & 41.91 & 56.66 & 66.46 (\(-\)2.14) \\ PG + Event & **34.41** & **63.25** & **43.58** & **59.69** & **67.97** (\(-\)0.63) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results on MSR dataset. to generate good Korean sentences with a small dataset since Korean is a morphologically complex language. However, even for this dataset, the proposed model outperforms its competitors. Table 6 presents some examples of compressed sentences by 'PG' and 'PG + Event'. Sentence 1 and 2 in this table are from MSR dataset, while Sentence 3 comes from Filippova dataset. Bold words indicate event words in all source sentences. The important phrases of Sentence 1 are _'80% of youth will report increased supervised time in safe environments'_ and _'80% of participants will report increased conflict resolution skills'_, and the phrases contain the event word _'report increased'_. The compressed sentence by 'PG' is _\"Anticipated outcomes from the spring survey include supervised increased conflict % of participants will report increased conflict resolution.\"_ which misses the important information such as _'supervised time in safe environments'_ and thus delivers distorted meaning from the source sentence. On the other hand, the compressed sentence by the proposed 'PG + Event' delivers more precise meaning than that by 'PG', since it contains _'80% of youth will report in safe environments'_ and _'they will report increased conflict resolution skills. '_  
To verify the attention weights when decoding, the weights for Sentence 1 are shown in Figure 2. The figure shows the source sentence in the horizontal axis and the generated sentence in the vertical axis. The darker the color of each word cell is, the heavier the attention weight of the word is. The upper figure shows the compressed sentence by 'PG' and the bottom one is by 'PG + Event'. The bold tokens are those recognized as event words. In the attention by 'PG', after the word _'include'_, attention should have given to _'youth'_ and _'increased supervised time'_ or _'in the safe environments'_, but is given to a wrong word _'conflict'_. As a result, 'PG' generates _'participants will report increased conflict resolution'_. In the bottom figure, the attention weights by 'PG + Event' are relatively ideal. The model is attentive to all event words, and the words are generated in the compressed sentence. In addition, globally important phrases such as _'youth'_, _'safe environments'_, and _'conflict resolution skills'_ get attentive, and then the