Objective: Extractive Summarization, extracts the most applicable sentences from the main document, while keeping the most vital information in the document. The Graph-based techniques have become very popular for text summarisation. This paper introduces a hybrid graph based technique for single-document extractive summarization. Methods/Statistical Analysis: Prior research that utilised the graph-based approach for extractive summarisation deployed one function for computing the necessary summary. Nonetheless, in our work, we have recommended an innovative hybrid similarity function (H), for estimation purpose. This function hybridises four distinct similarity measures: cosine similarity (sim1), Jaccard similarity (sim2), word alignmentbased similarity (sim3) and the window-based similarity measure (sim4). The method uses a trainable summarizer, which takes into account several features. The effect of these features on the summarization task is investigated. Findings: By combining, t