A Knowledge Graph Based Approach for Automatic Speech and Essay Summarization
| Autor: | Sitalakshmi Venkatraman, Kunal Khadilkar, Siddhivinayak Kulkarni |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 05 social sciences Unstructured data Of the form 02 engineering and technology computer.software_genre Automatic summarization Knowledge graph Named-entity recognition 0502 economics and business 0202 electrical engineering electronic engineering information engineering Key (cryptography) Automatic speech Semantic memory 020201 artificial intelligence & image processing Artificial intelligence business computer 050203 business & management Natural language processing |
| Zdroj: | 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). |
| DOI: | 10.1109/i2ct45611.2019.9033908 |
| Popis: | Every day, big amounts of unstructured data is generated. This data is of the form of essays, research papers, speeches, patents, scholastic articles, book chapters etc. In today’s world, it is very important to extract key patterns from huge text passages or verbal speeches. This paper proposes a novel method for summarizing multilingual vocal as well as written paragraphs and speeches, using semantic Knowledge Graphs. Using the proposed model, big text extracts or speeches can be summarized for better understanding and analysis. The method uses speech recognition as well as Named Entity Recognition to identify entities from spoken content to create optimized Knowledge Graphs in the English Language. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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