Extra-iNet：一个针对文本预测的解释抽取框架

A self-explanatory sentence extraction framework(Extra-iNet) was proposed, which relied solely on coarse-grained task-level labels to extract certain sentences from input documents to guide document label prediction and provide human-readable explanations for the prediction results. This framework utilized a convolutional neural network to encode input sentences, and integrated the extraction and representation of predictive explanatory sentences into a single module. Reinforcement learning and gating mechanisms were adopted as two strategies to extract the explanatory subset, resulting in two variant models, namely Hard Extra-iNet and Soft Extra-iNet. The predictive and explanatory capabilities of the models were validated on sentiment analysis task and cumulative abnormal return prediction task. The results show that, compared to the baselines, the model's F₁ (the harmonic mean of precision and recall) in sentiment analysis task increases by an average of 15.5%, and its accuracy in cumulative abnormal return prediction task increases by an average of 3.7%. The results extracted by the model are highly consistent with the results annotated by humans. Extra-iNet framework can effectively extract explanatory sentences from documents and can be applied to the prediction in other fields.