# Abstractive Sentence Compression with Event Attention
## 1 Introduction  
Sentence compression is an NLP task which aims at generating a compact sentence from a long and complex source sentence while preserving the important content of the source sentence. Since it generates a shorter and more condensed sentence than its source sentence, it is also known as sentence-level summarization. The need to sentence compression is increasing with the rapid growth of web and mobile contents, since sentence compression allows web and mobile users to efficiently catch and understand the contents. That is, sentence compression does not only enhance the comprehensibility and readability of the contents for the users, but also saves time and cost [1, 2]. Thus, sentence compression is regarded as a significant and useful technique. There have been many studies about sentence compression and the studies are separated into two approaches: a deletion-based approach and an abstractive approach. The deletion-based approach generates a target sentence by removing unimportant and unnecessary words from the source sentence [3, 4, 5, 6, 7, 8]. That is, a target compressed sentence is a subsequence of a source sentence. [PERSON] et al. solved sentence compression as a binary classification task with a LSTM-based model [9]. In this model, LSTM(Long Short-term Memory) determines whether each word in a source sentenceshould be removed or not. This approach is straightforward, but dissimilar to sentence compression of human beings in that the target sentence by human beings often contains some new words that do not appear in the source sentence. In addition, the approach often produces a broken sentence by a few grammatical errors. This phenomenon gets severer in morphologically complex languages such as Korean and Japanese. Therefore, the approach is not appropriate for expressive sentence compression. Some abstractive compression methods have been proposed to solve the problem [10; 11; 12; 13; 14]. With the growth of neural networks in recent years, [PERSON] et al. proposed an abstractive compression model with sequence-to-sequence learning and an attention mechanism [15]. They used two kinds of decoders. One is a deletion decoder and the other is a copy-generation decoder. When the encoded vector of a source sentence is decoded, it is first passed to the deletion decoder. After that, the vector is incorporated with the result of deletion decoder, and the incorporated vector is fed to the copy-generation decoder. Although this is somewhat similar to human compression, it has some problems. First, the errors become cumulative, since the copy-generation decoder generates a target sentence based on the result of the deletion decoder. Thus, if the deletion decoder produces some errors, it is difficult for the copy-generation decoder to generate a correct target sentence. In addition, its attention mechanism often fails to focus on important content of a source sentence. In particular, when a source sentence is long, the attention failure gets severer [8; 16; 17]. This paper proposes an abstractive sentence compression model with an event attention. This model is based on the sequence-to-sequence model [16; 18] and the pointer generator [19] to generate an expressive target sentence, and adopts an event attention to focus on important eventual content in the source sentence. One of the major applications of sentence compression is to summarize news articles in which event words play a key role. Therefore, the event attention regards event words as influential words for sentence compression and focuses on them in decoding the vector of a source sentence. Even though event words provide key information for knowing what is going on in a source sentence, it is also important to understand a source sentence globally. Thus, the global attention [20] is also used to capture the global information of a source sentence. Then, the final attention is represented by combining the event attention and the global attention.