Fuzzy Inference Propelled Sentence Ranking for Extractive Summary Generation

Natural language serves as an impeccable tool for the appropriate representation of knowledge among individuals. Owing to the varying representation of the same knowledge base and the perpetual growth of the World Wide Web, the need to uncover an effective method to condense available textual data without significantly dampening the implied information is paramount. In an attempt to solve the need for effectively condensing textual data, the paper proposes a system which is capable of mimicking the human brain's approach to process Natural Language Fuzzy Logic. The system is subjected to both intrinsic and extrinsic evaluation and the results are compared against two other text summarizers - Auto summarize Tool and SweSum using the CNN Corpus Dataset. The Relevance Prediction Measure, F1 Score and Recall results suggest the applicability of Fuzzy Reasoning in text summarization and through evaluation, it can be inferred that proposed system has successfully tried to mimic the process of summary generation by the human brain.

Novel intelligent adjustment height method of Shearer drum based on adaptive fuzzy reasoning Petri net

The complexity of the coalface environment determines the non-linear and fuzzy characteristics of the drum adjustment height. To overcome this challenge, this study proposes an adaptive fuzzy reasoning Petri net (AFRPN) model based on fuzzy reasoning and fuzzy Petri net (FPN) and then applies it to the intelligent adjustment height of the shearer drum. This study constructs adaptive and reasoning algorithms. The former was used to optimize the AFRPN parameters, and the latter made the AFRPN model run. AFRPN could represent rules that had non-linear and attribute mapping relationships and could adjust the parameters adaptively to improve the accuracy of the output. Subsequently, the drum adjustment height model was established and compared to three models neural network (NN), classification and regression tree(CART) and gradient boosting decision tree (GBDT). The experimental results showed that this method is superior to other drum adjustment height methods and that AFRPN can achieve intelligent adjustment of the shearer drum height by constructing fuzzy inference rules.