We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In addition, we also explore the effectiveness of the writer information for generation. Experimental results show that our model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.

Comments on a stock market often include the reason or cause of changes in stock prices, such as "Nikkei turns lower as yen's rise hits exporters." Generating such informative sentences requires capturing the relationship between different resources, including a target stock price. In this paper, we propose a model for automatically generating such informative market comments that refer to external resources. We evaluated our model through an automatic metric in terms of BLEU and human evaluation done by an expert in finance. The results show that our model outperforms the existing model both in BLEU scores and human judgment.

This paper deals with the query-biased summarization task. Conventional non-neural network-based approaches have achieved better performance by primarily including the words overlapping between the source and the query in the summary. However, recurrent neural network (RNN)-based approaches do not explicitly model this phenomenon. Therefore, we model an RNN-based query-biased summarizer to primarily include the overlapping words in the summary, using a copying mechanism. Experimental results, in terms of both automatic evaluation with ROUGE and manual evaluation, show that the strategy to include the overlapping words also works well for neural query-biased summarizers.

Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., "Nikkei gains" is generated when "Nikkei drops" is expected. We investigate models trained on contrastive examples, that is, incorrect sentences or terms, in addition to correct ones to reduce such errors. We first create rules to produce contrastive examples from correct ones by replacing frequent crucial terms such as "gain" or "drop". We then use learning methods with several losses that exploit contrastive examples. Experiments on the market comment generation task show that 1) exploiting contrastive examples improves the capability to generate sentences with better lexical choices, without degrading the fluency, 2) the choice of the loss function is an important factor because the performances of different metrics depend on the types of loss functions, and 3) the use of the examples produced by some specific rules further improves performance. Human evaluation also supports the effectiveness of using contrastive examples.