SemPCA-Summarizer: Exploiting Semantic Principal Component Analysis for Automatic Summary Generation

Text summarization is the task of condensing a document keeping the relevant information. This task integrated in wider information systems can help users to access key information without having to read everything, allowing for a higher efficiency. In this research work, we have developed and evaluated a single-document extractive summarization approach, named SemPCA-Summarizer, which reduces the dimension of a document using Principal Component Analysis technique enriched with semantic information. A concept-sentence matrix is built from the textual input document, and then, PCA is used to identify and rank the relevant concepts, which are used for selecting the most important sentences through different heuristics, thus leading to various types of summaries. The results obtained show that the generated summaries are very competitive, both from a quantitative and a qualitative viewpoint, thus indicating that our proposed approach is appropriate for briefly providing key information, and thus helping to cope with a huge amount of information available in a quicker and efficient manner

A unified formal framework for factorial and probabilistic topic modelling

Topic modelling has become a highly popular technique for extracting knowledge from texts. It encompasses various method families, including Factorial methods, Probabilistic methods, and Natural Language Processing methods. This paper introduces a unified conceptual framework for Factorial and Probabilistic methods by identifying shared elements and representing them using a homogeneous notation. The paper presents 12 different methods within this framework, enabling easy comparative analysis to assess the flexibility and how realistic the assumptions of each approach are. This establishes the initial stage of a broader analysis aimed at relating all method families to this common framework, comprehensively understanding their strengths and weaknesses, and establishing general application guidelines. Also, an experimental setup reinforces the convenience of having harmonized notational schema. The paper concludes with a discussion on the presented methods and outlines future research directions.Peer ReviewedPostprint (published version