# Abstractive Sentence Compression with Event Attention
## 2 Related Work  
There have been several studies on sentence compression and the studies are separated into two kinds of approaches: the deletion-based approach and the abstractive approach. Sentence compression by deletion-based approach is to decide whether each word in a source sentence remains in a target sentence. That is, it regards sentence compression as a binary sequence labeling problem. Linguistic information is usually used to determine the sequence labels [25; 26; 27], but some previous studies compressed a sentence by pruning the dependency parse tree of a source sentence [7; 10]. [PERSON] et al. proposed an unsupervised compression method to prune the dependency parse tree [28]. In their method, the weights of the edges in a dependency parse tree are computed using syntactic and length constraints, and then they are used to prune the dependency tree. After that, they proposed a supervised version which extends the unsupervised method [6]. For supervised learning of sentence compression, they also released a dataset for sentence compression. In this version, the weight of every edge in a parse tree is computed by a linear function of lexical, syntactic, and semantic features, and the relevance weights of the features are determined using the released data. All these models depend greatly on the accuracy of parsing a source sentence, but current techniques for parsing natural language sentences are not fully trustworthy yet. With the growth of neural networks, some studies have adopted a neural network to determine word drops. [PERSON] et al. claimed that syntactic information could be unnecessary for sentence compression [9]. Thus, they adopted a LSTM model to determine if a word should be deleted in the compressed sentence based on the deletion/retention of its previous word in a source sentence. In addition, [PERSON] et al. used a LSTM constrained with a target length [29]. These methods do not use any syntactic information, but some experiments showed that the methods could be improved if syntactic information is incorporated into the methods [7]. One of main problems using neural networks for sentence compression is that named entities in a source sentence are usually considered to be unknown words since the neural models use a pretrained word embedding, where high frequency of unknown words often leads to performance decrease of the models. [PERSON] et al. hypothesized that the incorporation of syntactic information into a compression model would be helpful in sentence compression and domain adaptability [7]. Thus, they proposed a LSTM-based model which uses syntactic information such as POS tags and parsing information. They showed empirically that syntactic information is influential and leverages the robustness of a model in cross-domain applications. However, the deletion-based approach has the chronic limitation of being unable to generate expressive sentences. The abstractive approach to sentence compression can be regarded as sentence paraphrasing. Thus, it can generate some unseen words and deliver more expressiveness in the target compressed sentence. That is, it produces more expressive compressed sentences than the deletion-based approach. Inspired by neural machine translation, [PERSON] et al. applied an attention-based sequence-to-sequence model to sentence compression [11]. Since a sequence-to-sequence model is structurally simple, their model has the merit that it does not require any pre- and post-processing for sentence compression. On the other hand, [PERSON] et al. adopted a memory-augmented recurrent neural network [30]. The memory-augmented model uses a memory matrix to get a better understanding of a source sentence. However, these studies are vulnerable to named entities since they generate a sentence with a fixed vocabulary set. To overcome this problem, [PERSON] et al. proposed an operation network that mimics human sentence compression [15].