Topic- Title designing and implementing web mining techniques. An abstract of 2 Pages thesis on automatic document summarization Need to write a fresh one with the help of the abstract attached
Abstract
With the invent of Internet the web has become a place where every individuals, company, governments put information, report which are for public. This ocean is increasing exponentially every day. To find any information, reading the whole document is almost important task. Web Mining is the science in which we mine the information from web store, web documents using different patterns found in these. Web Content Mining is one of the important task in the three types of mining job Web usage Mining, Web Structure Mining and Web Content Mining. To find the accurate, correct, precise summary automatically created is one of the task in Web Content Mining.
Automatic summary has following goals
- creation of precise summary of a given document.
- ii) creation of summary from multiple documents available on one topic.
In this research work we presented the techniques to create the summary
- Creation of single document summary using calculation of Pointwise Mutual information for each sentence of a document
- Redundancy and Coverage Aware Enriched Dragonfly-FL Single Document Summarization
- Creation of single document summary using Extractive multi-document text summarization using dolphin swarm optimization approach
The single document summary created using pointwise mutual information, calculates the point wise mutual information between the consecutive words of every sentences and calculates the total PMI of every sentence. It is found that this produces a good quality summaries when we compare these summaries with few online available tools such as tools4noob.com, brevity summarizer.
Coverage aware enriched dragonfly-FL single document summarization is used to improve the result. The performance of it is evaluated by testing the algorithm against MAMHOA, ExDoS, Karci summarization, and regression-based technique. It can be perceived that the precision, recall, and F-score of the proposed Enriched Dragonfly Fuzzy Logic (FL) summarization technique is higher than the existing techniques.
Dolphin swarm optimization is used to create summaries from multiple document. The proposed method maximizes the relevancy and reduces the redundancy using the Dolphin Swarm optimization algorithm has been calculated in the context of MD summarization on the multiling 2013 datasets. To compare the performance of “Maximum coverage and relevancy with minimal redundancy (MCRMR)” with shark smell optimization (SSO), MCRMR with “particle swarm optimization (PSO)”, by adjusting the controlling factors utilizing the adaptive method. These results demonstrate that the suggested technique performs more correctly when the elimination procedure is not taken into consideration. Afterward, they compared the impacts of the suggested strategy with the results acquired without going through the derivation procedure.
The performance of the proposed Dragonfly-FL summarization is tested in CNN/Daily mail dataset and finally, the results are compared with the existing techniques in terms of ROUGE-1, ROUGE-2, and ROUGE-L measures. The observation demonstrates that the proposed technique performs better than the existing techniques.
The proposed approach is tested under python with multiling 2013 dataset and the performances have been evaluated with ROUGE and AutoSummENG metrics. The investigational outcomes show that the proposed technique works well and very much effective for multi-document text summarization.
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