# A Topical Category-Aware Neural Text Summarizer
## 2 Related Work  
The main approach to text summarization at the early stage was the extractive summarization which extracts key words or sentences from an original text and then generates a summary by rearranging them. Before appearance of neural sequence-to-sequence models, two types of methods were mainly used to sort out important sentences from an original text. One is to use specific sentence features for scoring sentences such as term frequency, TF:IDF weight, sentence length, and sentence position [14], and the other is to adopt a specific ranking method such as a structure-based, a vector-based, or a graph-based method [15]. After development of the neural summarization [8; 16], there have been many studies that use a neural network for sentence modeling and scoring [1; 17] or use a neural sequence classifier to determine which sentences should be included in the summary [10]. However, the extractive summarization suffers from two kinds of problems. One is that it generates a grammatically incorrect output when over-extraction occurs, and the other is that the summary does not have a natural flow when uncorrelated words or sentences are extracted.  
The abstractive summarization which contrasts with the extractive summarization generates a summary through understanding an original text and producing abstract sentences that reflect the understanding. This kind of summarization has been studied relatively less than the extractive summarization because of its difficulty. However, the research on abstractive summarization has been active after [PERSON] et al. applied neural networks to abstractive summarization and showed significant performance improvement over the extractive summarization [9]. [PERSON] et al. used a recurrent neural network (RNN) for the first time to generate a summary and proved empirically that RNN is more suitable for abstractive summarization than feed-forward networks [18]. On the other hand, [PERSON] et al. focused on the sequence processing ability of RNN and thus proposed a sequence-to-sequence model in which a RNN is used as both an encoder and a decoder [2]. While RNNs show promising results, they suffer from the out-of-vocabulary (OOV) problem and tend to generate the same words repeatedly. Thus, [PERSON] et al. solved the OOV problem by the pointer network [19] and the repetition problem by coverage mechanism [20] which discourages repetitions by keeping track of what has been summarized [5].  
Topic category is regarded as a key information of a text. Thus, it is not unusual to improve the summary quality by controlling a summarizer to reflect a topic category into a summary [21; 22; 23; 24]. [PERSON] et al. proposed a neural summarizer that takes an article along with its topic as its input [21], but the summarizer has a problem of requiring multiple summaries of a document for every topic. It is expensive to prepare the data in which a single text has multiple summaries for various topics, since it is a very tedious and laborious task to write summaries manually for every topic. Thus, they proposed a method to create such data artificially in this work. However, the quality of automatically generated summaries is less reliable than that of manual summaries. Therefore, in this paper, we propose a model that reflects a topic into a summary, but does not require a different summary for every different topic.