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The massive upload of text on the internet creates a huge inverted index ininformation retrieval systems, which hurts their efficiency. The purpose ofthis research is to measure the effect of the Multi-Layer Similarity model ofthe automatic text summarization on building an informative and condensedinvert index in the IR systems. To achieve this purpose, we summarized aconsiderable number of documents using the Multi-Layer Similarity model, and webuilt the inverted index from the automatic summaries that were generated fromthis model. A series of experiments were held to test the performance in termsof efficiency and relevancy. The experiments include comparisons with threeexisting text summarization models; the Jaccard Coefficient Model, the VectorSpace Model, and the Latent Semantic Analysis model. The experiments examinedthree groups of queries with manual and automatic relevancy assessment. Thepositive effect of the Multi-Layer Similarity in the efficiency of the IRsystem was clear without noticeable loss in the relevancy results. However, theevaluation showed that the traditional statistical models without semanticinvestigation failed to improve the information retrieval efficiency. Comparingwith the previous publications that addressed the use of summaries as a sourceof the index, the relevancy assessment of our work was higher, and theMulti-Layer Similarity retrieval constructed an inverted index that was 58%smaller than the main corpus inverted index.

The Effect of the Multi-Layer Text Summarization Model on the Efficiency and Relevancy of the Vector Space-based Information Retrieval