Fuzzy Inference Propelled Sentence Ranking for Extractive Summary Generation

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Fuzzy Logic ◽  
Natural Language ◽  
Human Brain ◽  
World Wide ◽  
Fuzzy Inference ◽  
Fuzzy Reasoning ◽  
Text Summarization ◽  
Textual Data ◽  
The World ◽  
Representation Of Knowledge

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.

scholarly journals Guest Editorial

1997 ◽  
Vol 12 (3) ◽  
pp. 229-230 ◽  
Author(s):  
DAVE STUART ROBERTSON
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
World Wide ◽  
Guest Editorial ◽  
Human Life ◽  
Knowledge Based Systems ◽  
Fuzzy Logic Controllers ◽  
Incorrect Decision ◽  
Knowledge Based ◽  
The World

Knowledge based systems are used in applications where an incorrect decision could put human life in jeopardy. A quick trawl through the World Wide Web is sufficient, these days, to locate such applications in design, analysis and testing; protection advice; operator decision support; signal monitoring; embedded systems and others. Depending on the type of system, these either give information which is not guaranteed to be correct (in many operator support applications) or which is imprecise (for example in fuzzy logic controllers).

scholarly journals Identification and Effective Summary Extraction with Deduplication of Data in News Articles

2020 ◽  
pp. 96-102
Author(s):  
Rajat Bodankar ◽  
Mayuri Waghmare
Keyword(s):  
Natural Language Processing ◽  
World Wide Web ◽  
Natural Language ◽  
Language Processing ◽  
World Wide ◽  
The World

Text summary, which is the most prominent application for data pressure, is provided for natural language processing. Content rundown is a process for the summary of the unique archive measurement by reducing the number of vital data from a uniquely reported report. In less time, a need emerges that the development of information increases greatly on the World Wide Web or on desktops of customers so that the multi-document overview is the best way of summarising it in less time. This paper presents an examination of existing procedures with the odds of stressing the need for an intelligent multi-document resumer.

Question Answering from Procedural Semantics to Model Discovery

2006 ◽  
pp. 479-485
Author(s):  
John Kontos ◽  
Ioanna Malagardi
Keyword(s):  
Natural Language ◽  
World Wide ◽  
Question Answering ◽  
Knowledge Bases ◽  
Procedural Semantics ◽  
Short Answer ◽  
Text Collections ◽  
The World ◽  
Fact Retrieval ◽  
Parameter Values

Question Answering (QA) is one of the branches of Artificial Intelligence (AI) that involves the processing of human language by computer. QA systems accept questions in natural language and generate answers often in natural language. The answers are derived from databases, text collections, and knowledge bases. The main aim of QA systems is to generate a short answer to a question rather than a list of possibly relevant documents. As it becomes more and more difficult to find answers on the World Wide Web (WWW) using standard search engines, the technology of QA systems will become increasingly important. A series of systems that can answer questions from various data or knowledge sources are briefly described. These systems provide a friendly interface to the user of information systems that is particularly important for users who are not computer experts. The line of development of ideas starts with procedural semantics and leads to interfaces that support researchers for the discovery of parameter values of causal models of systems under scientific study. QA systems historically developed roughly during the 1960-1970 decade (Simmons, 1970). A few of the QA systems that were implemented during this decade are: • The BASEBALL system (Green et al., 1961) • The FACT RETRIEVAL System (Cooper, 1964) • The DELFI systems (Kontos & Kossidas, 1971; Kontos & Papakontantinou, 1970)

scholarly journals Fuzzification Based Osteoporosis Prediction Model

2021 ◽  
Vol 10 (6) ◽  
pp. 28-31
Author(s):  
Shilpa Kumar ◽  
Shubangi D C
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Dual Energy ◽  
Literature Survey ◽  
Inference System ◽  
X Ray ◽  
The World

Osteoporosis is a disease in which bones become fragile and more likely to break. Osteoporosis can progress painlessly until it causes a bone fracture or a bone break. Dual Energy X-ray Absorptiometry (DEXA) is more costly and not accessible easily so we are using Fuzzy Inference system to predict osteoporosis. In this fuzzy logic, we collect risk factors and rules for osteoporosis and build a interface which take inputs and predicts if a person has osteoporosis. In the following Literature survey, we will take risk factors, rules, and ways to implement them. Around the world, 33% of women and 20% men over the age of 50 will suffer a fracture caused by Osteoporosis. Osteoporosis is a disease in which Bones become shallow and are fractured. If predicted before, quality of life will increase and severe surgery may be avoided.

scholarly journals Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Nicholas A. I. Omoregbe ◽  
Israel O. Ndaman ◽  
Sanjay Misra ◽  
Olusola O. Abayomi-Alli ◽  
Robertas Damaševičius
Keyword(s):  
Fuzzy Logic ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Diagnosis ◽  
Text Messaging ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
System Usability Scale

The use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.

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