# A Topical Category-Aware Neural Text Summarizer
## 5 Experiments  
### Data Set  
The New York Times Annotated Corpus (NYT Corpus) [27] which is a collection of The New York Times articles from 1 January 1987 to 19 June 2007 is a representative corpus for text summarization which has a category and a summary for each article. The number of articles in this corpus is 1,238,889, and each article in this corpus is annotated with a summary and two types of topic categories. One topic type is a general online descriptor and the other is online section. The general online descriptor is known to be automatically assigned by an algorithm and then manually verified by a production staff. The online section of an article shows where the article is posted on NYTimes.com, and represents a coarse topic. In the experiments below, the general online descriptor is adopted as a topic category of articles, since it classifies the articles more finely and precisely than the online section. The NYT Corpus has 776 general online descriptors, but only top seven descriptors are used for the sake of learning efficiency. The descriptors used are \"Politics and Government\", \"Finances\", \"Medicine and Health\", \"Books and Literature\", \"Music\", \"Baseball\", and \"Education and Schools\".  
Several pre-processing steps are applied to prepare a data set for text summarization from the corpus. Note that all articles in the corpus do not have a summary. Thus, the articles without a summary are first excluded from the data set. In addition, the articles whose length is less than 15 words or which have a summary of fewer than three words are excluded since such articles do not provide enough context. The number of articles which have a summary is 460,419, but only 183,721 articles remain after these pre-processing steps. Then, all articles and summaries are limited to have up to 800 and 100 words respectively following the study of [PERSON] et al. [6]. All alphabets are represented as lower-cases and some garbage words in the summaries are removed. The markers of \"photo\", \"graph\", \"chart\", \"map\", \"table\", \"drawing\" at the end of the summaries are also removed. Table 2 shows a simple statistics on the data set after all these-processing steps. The average number of tokens in articles is 500.75 and that in summaries is 40.74. The data set is further split into a training, a validation, and a test set after random shuffling as shown in Table 3. 80% (146,977 articles) of the articles in the data set is used as a training set, 10% (18,372 articles) as a validation set, and the remaining 10% (18,372 articles) as a test set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **No. of Articles** & **Avg. Tokens in Articles** & **Avg. Tokens in Summaries** \\ \hline Politics \& Government & 66,698 & 500.11 & 47.62 \\ Finances & 28,540 & 479.65 & 46.46 \\ Medicine \& Health & 25,516 & 467.85 & 45.04 \\ Books \& Literature & 19,807 & 545.64 & 27.67 \\ Music & 17,138 & 504.06 & 27.83 \\ Baseball & 13,051 & 623.50 & 24.23 \\ Education \& Schools & 12,971 & 419.51 & 38.05 \\ \hline Total & 183,721 & 500.75 & 40.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simple statistics on the New York Times (NYT) Corpus.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **No. of Articles** & **Avg. Tokens in Articles** & **Avg. Tokens in Summaries** \\ \hline Training set & 146,977 & 501.05 & 40.78 \\ Validation set & 18,372 & 497.04 & 40.43 \\ Test set & 18,372 & 501.01 & 40.73 \\ \hline Total & 183,721 & 500.75 & 40.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simple statistics on the training, validation, and test set.