Implicit Sensitive Text Summarization based on Data Conveyed by Connectives
This addresses the limitation of current summarizers that ignore author intent and implicit cues, which is an incremental step for natural language processing applications.
The paper tackles the problem of capturing implicit information in automatic text summarization, particularly focusing on argumentative connectives like 'but' and 'yet', and presents a system designed to acquire this implicit knowledge to improve summaries.
So far and trying to reach human capabilities, research in automatic summarization has been based on hypothesis that are both enabling and limiting. Some of these limitations are: how to take into account and reflect (in the generated summary) the implicit information conveyed in the text, the author intention, the reader intention, the context influence, the general world knowledge. Thus, if we want machines to mimic human abilities, then they will need access to this same large variety of knowledge. The implicit is affecting the orientation and the argumentation of the text and consequently its summary. Most of Text Summarizers (TS) are processing as compressing the initial data and they necessarily suffer from information loss. TS are focusing on features of the text only, not on what the author intended or why the reader is reading the text. In this paper, we address this problem and we present a system focusing on acquiring knowledge that is implicit. We principally spotlight the implicit information conveyed by the argumentative connectives such as: but, even, yet and their effect on the summary.