# Abstractive Sentence Compression with Event Attention
## 6 Conclusions  
We have proposed an abstractive sentence compression model with event attention. Sentence compression is the task of generating a compact sentence from a source sentence while preserving the important content of the source sentence. However, existing models for sentence compression have a limitation that their attention often fails to focus on important context of a source sentence especially when the source sentence is long and complex. In this paper, we handled the problem with event attention. The proposed event attention focuses on event words since event words are important information for sentence compression and deliver the meaning of source sentences. In addition to event attention, the global attention was also used which helps to understand source sentences because it captures global information of the source sentence. Therefore, the proposed model compresses source sentences by combining event and global attention. For the evaluation of the proposed model, the proposed model has been compared with two baselines on three standard datasets. According to the experimental results, it outperforms all baselines for all datasets of MSR dataset, Filippova dataset, and Korean sentence compression dataset. In particular, it shows 122% higher BLEU score than the sequence-to-sequence model on MSR dataset. This result shows that event words are valuable information and the proposed model is effective for sentence compression. For future work, we will extend the proposed model for multi-sentence compression. The current model compresses just one or two sentences, but real news articles consist of multi-sentences. Thus, multi-sentence compression is required for real applications.  
Figure 3: The attention weights on Sentence 3 in Table 6.  
Supplementary Materials: The MSR dataset [21] and Filippova dataset [9] are used to support this study and are available at [[https://www.microsoft.com/en-us/research/project/intelligent-editing](https://www.microsoft.com/en-us/research/project/intelligent-editing)]([https://www.microsoft.com/en-us/research/project/intelligent-editing](https://www.microsoft.com/en-us/research/project/intelligent-editing)) and [[https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)]([https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)). The Korean sentence compression dataset generated during the current study are available in [KoreanSentenceCompression] repository ([[https://github.com/daisy-choi/KoreanSentenceCompression](https://github.com/daisy-choi/KoreanSentenceCompression)]([https://github.com/daisy-choi/KoreanSentenceCompression](https://github.com/daisy-choi/KoreanSentenceCompression))).  
Conceptualization, S.J.C. and I.J.; Funding acquisition, S.P.; Investigation, S.J.C. and I.J.; Methodology, S.J.C. and I.J.; Project administration, S.P.; Supervision, S.P. and S.-B.P.; Visualization, S.J.C.; Writing--original draft, S.J.C.; Writing--review & editing, S.-B.P.  
This study was supported by the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005).  
The authors declare no conflict of interest.