{'fraction_non_alphanumeric': 0.01167728237791932, 'fraction_numerical': 0.0, 'mean_word_length': 5.286666666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Intelligent Patent Text Summarization Analysis Method

Patent mining and patent analysis of patented technologies will help protect the interests of intellectual property rights and provide enterprises with correct scientific research directions. In order to study the profitable patents of pharmaceutical companies, this paper proposes an Abstractive RL-LSTM neural network method based on patent texts. The reinforcement learning method is introduced into LSTM. The purpose is to rely on Q-learning to learn the relationship between the main layers. The two parallel layers share the weight of attention from the Q value, and realize the hierarchical control between the LSTM structure of the patent document and the LSTM structure of the sentence. The experimental results show that compared with other methods, the method proposed in this paper can further improve the ROUGE index and alleviate the dependence of the decoder on the input.