He majorchallenge in incremental learning is to learn from new data without accessing the previously observed data.This property fosters the stability-plasticity dilemma, where stability describes retaining the previously acquiredknowledge, and plasticity describes learning knowledge from new data. So, an ideal approach for incremental learning must find a balance between stability and plasticity. Focussing only on plasticity may lead to a situation, called catastrophic forgetting. At the same time, concentrating onlyon stability may lead to loss of knowledge. Only the relevant previous knowledge must be preserved. Irrelevant previous knowledge must be discarded, but with the ability that it can be recalled whenever required. Another challenge in incremental learning is learning in the presence of concept drift.

Presentations are one of the most common and effective ways of communicating the overview of a work to the audience. Given a technical paper, automatic generation of presentation slides reduces the effort of the presenter and helps in creating a structured summary of the paper. In this paper, we propose the framework of a novel system that does this task. Any paper that has an abstract and whose sections can be categorized under introduction, related work, model, experiments and conclusions can be given as input. As documents in LaTeX are rich in structural and semantic information we used them as input to our system. These documents are initially converted to XML format. This XML file is parsed and information in it is extracted. A query specific extractive summarizer has been used to generate slides. All graphical elements from the paper are made well use of by placing them at appropriate locations in the slides. These slides are presented in the document order.