We propose a global optimization method under length constraint (GOLC) for neural text summarization models. GOLC increases the probabilities of generating summaries that have high evaluation scores, ROUGE in this paper, within a desired length. We compared GOLC with two optimization methods, a maximum log-likelihood and a minimum risk training, on CNN/Daily Mail and a Japanese single document summarization data set of The Mainichi Shimbun Newspapers. The experimental results show that a state-ofthe-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6.70% overlength summaries on CNN/Daily and 7.8% on long summary of Mainichi, compared to the approximately 20% to 50% on CNN/Daily Mail and 10% to 30% on Mainichi with the other optimization methods. We also demonstrate the importance of the generation of in-length summaries for post-editing with the dataset Mainich that is created with strict length constraints. The experimental results show approximately 30% to 40% improved post-editing time by use of inlength summaries.

We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical compound paraphrase model. Our method enables the long short-term memory (LSTM) of the NER model to capture chemical compound paraphrases by sharing the parameters of the LSTM and character embeddings between the two models. The experimental results on the BioCreative IV's CHEMDNER task show that our method improves chemical NER and achieves state-of-the-art performance (+1.43 F-score).

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We propose a new model for the guided text summarization task. In this task, it is required that a generated summary covers all the aspects, which are predefined for the topic of the given document cluster; for example, aspects for the topic "Accidents and Natural Disasters" include WHAT, WHEN, WHERE, WHY, WHO AFFECTED, DAMAGES and COUNTERMEASURES. We use as a scorer for an aspect, the maximum entropy classifier that predicts whether each sentence reflects the aspect or not. We formalize the coverage of the aspects as a max-min problem, which enables a summary to cover aspects in a well-balanced manner. In the max-min problem, the minimum of the aspect scores is going to be maximized so that the summary contains all the aspects as much as possible. Furthermore, we integrate the model based on the max-min problem with the maximum coverage summarization model, which generates a summary containing as many conceptual units as possible. Through the experiments on benchmark datasets for the guided summarization, we show that our model outperforms other approaches in terms of ROUGE-2.

Background Cognitive behavioral therapy for obsessive-compulsive disorder has been established, but access to this therapy in Japan is limited. Internet-based cognitive behavioral therapy may improve treatment accessibility and sufficiently improve obsessive-compulsive symptoms. There are few randomized controlled trials examining the effectiveness of internet-based cognitive behavioral therapy in patients with obsessive-compulsive disorder. We designed a randomized controlled trial protocol to assess the effectiveness of guided internet-based cognitive behavioral therapy in Japanese patients with obsessive-compulsive disorder. Objective We aimed to develop a protocol for a randomized controlled trial of internet-based cognitive behavioral therapy in Japanese patients with obsessive-compulsive disorder. Methods The randomized controlled trial will compare internet-based cognitive behavioral therapy treatment and usual care groups, each consisting of 15 participants (n=30) diagnosed with obsessive-compulsive disorder. We will evaluate the effectiveness of a 12-week intervention. The primary outcome of symptom severity will be measured using the Yale-Brown Obsessive-Compulsive Scale. Secondary outcomes will be assessed with the Obsessive-Compulsive Inventory, Beck Anxiety Inventory, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Working Alliance Inventory-Short Form, and the Euro Qol – 5 Dimension. All measures will be assessed at weeks 0 (baseline) and 12 (follow-up). In the statistical analysis comparing treatment effects, the least-squares means and their 95% CIs will be estimated by analysis of covariance with the change in total outcomes scores at week 12. All comparisons are planned, and all P values will be two-sided, with values <.05 considered statistically significant. Results The study will be performed from January 2020 to March 2021, and results are expected to be available in mid-2021. Conclusions The trial will demonstrate whether internet-based cognitive behavioral therapy improves access and is more effective than more usual care for patients with obsessive-compulsive disorder in Japan. Trial Registration University Hospital Medical Information Network (UMIN) 000039375; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000044422 International Registered Report Identifier (IRRID) DERR1-10.2196/18216