# A Topical Category-Aware Neural Text Summarizer
## 1 Introduction  
Due to the fast generation of text data by various media, it is becoming nearly impossible for a person to read all texts directly, which leads to the need for automatic text summarization. Thus, automatic text summarization is nowadays used widely in many applications of natural language processing, but is divided into two types depending on how a summary is generated. One type is the extractive summarization [1; 2; 3; 4] which selects important words, phrases, or sentences from an original text and combines them to create a summary. Since it uses the content of the original text directly, it is regarded to be a relatively easy way to summarize a text. However, the summaries by extractive summarization are likely to have low cohesion or readability. The other is the abstractive summarization [5; 6; 7] which generates summaries by comprehending the content of an original text. Since it is based on an overall understanding of the context, the problem of generating inconsistent summarization rarely occurs. In addition, the summaries by this type are likely to have high cohesion or readability, since abstractive summarization generates summaries by paraphrasing. However, abstractive summarizers have to write concise sentences through understanding of whole content. Therefore, it is in general more difficult to design an abstractive summarizer than to design an extractive one. Because of low difficulty, the early research on automatic text summarization has focused mainly on the extractive summarization [1; 2; 3; 4]. However, vigorous research about sequence-to-sequencemodels [8] makes it easy to generate a context vector that contains an entire content of an original text, where a sequence-to-sequence model is a neural network which is composed of an encoder and a decoder. When a sequence-to-sequence model is used as a summarization model, the encoder takes a sequence of original text as its input and generates a context vector as its output. Since the context vector is created by referencing all sequences of the input, the context vector encompasses the entire content of the input. Then, the decoder takes the context vector as its input and generates a summary as its output. This operation of the sequence-to-sequence model fits well with the abstractive summarization. As a result, the number of studies on neural abstractive summarization increases gradually [5; 7; 9; 10; 11]. One problem of sequence-to-sequence models for abstractive summarization is that they do not reflect any topical information into summarization even though most texts have one or more topical themes. Especially, the topic category of a text exists actually, though it is not revealed explicitly. As a result, the summaries by human beings are affected by a topic category. Table 1 shows two example summaries by human beings that prove that a topic category is critical information for text summarization. In this table, \(\mathbf{S}\) and \(\mathbf{R}\) indicate a source text and its summarized text, respectively. The first example is about music. The phrases related with music such as \"two concerts at the Metropolitan Museum of Art\" and \"pianist\" remain in the summary. On the other hand, the second example is about politics. Thus, the words such as \"Governor\" and \"campaign\" survive in the summary. As shown in these examples, it is of importance to consider a topic category in summarizing a text. This paper proposes a novel neural summarizer which compresses a text according to its topic category. The class activation map (CAM) [12] is adopted to incorporate a topic category into a summary. It is obtained by applying the global average pooling (GAP) to a convolutional neural network (CNN) so that it predicts which parts of an input instance should be highlighted to classify the instance to a specific topic category. The proposed summarizer is basically a neural encoder-decoder model of which encoder and decoder are a long short-term memory (LSTM).