Abstract
In today’s world, there is a massive amount of data being continuously generated every minute. This data can be utilised to gain a large amount of information that can have numerous uses. However, it is difficult to obtain this information because of the speed and volume of data being generated. One of the tools that can be useful in extracting useful information from textual data is a text summarization and analysis tool. Many text summarization tools are being developed but largely focus on summarising a single document effectively. This project aims to create a text summarization tool using abstractive and extractive text summarization techniques that can extract the relevant and important information from multiple documents and present it as a concise summary. The tool also performs multiple analyses on the data to obtain more useful information and make inferences based on the contents of the input textual data. This tool has various use cases as it can greatly reduce the time spent in gathering information from a large number of different documents such as surveys and feedback forms from various sources by providing an effective summary and analysis of the relevant data in these text documents.
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Bathija, R., Agarwal, P., Somanna, R., Pallavi, G.B. (2021). Multi-document Text Summarization Tool. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_63
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DOI: https://doi.org/10.1007/978-981-15-5258-8_63
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