Hybrid model of content extraction

We present a hybrid model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features like link density and text distribution across the node to predict significance of the node towards overall content provided by the document. Once significance of the nodes is determined, the formatting characteristics like fonts, styles and the position of the nodes are evaluated to identify the nodes with similar formatting as compared to the significant nodes. The proposed hybrid model is derived from two different models, i.e., one is based on statistical features and other on formatting characteristics and achieved the best accuracy. We describe the validity of model with the help of experiments conducted on the standard data sets. The results revealed that the proposed model outperformed other existing content extraction models. We present a browser based implementation of the proposed model as proof of concept and compare the implementation strategy with various state of art implementations. We also discuss various applications of the proposed model with special emphasis on open source intelligence.

► We propose Hybrid Model for Content Extraction.
► The proposed model exploits feature set consisting of statistical features and formatting characteristics.
► The experimental evaluations reveal that the proposed model achieved the best F1 score of 94.74%.
► We discuss the browser based implementation along with benefits.
► We discussed various applications of the proposed model emphasizing open source intelligence.