단어 간 의미적 연관성을 고려한 개선된 문서 요약 방법 연구

Abstract

Along with the recent development and distribution of smart devices, data contained in documents appearing on the internet are increasing exponentially. As documents are increasing exponentially, only the titles and main points are shown to users as a solution to figure out information in a document they want. Only with brief contents, however, users may find it difficult to get the information of a document they want. If document wanted by a user is expressed in an accurate form, it will be helpful when trying to find out necessary information. Document summarization is referred to eliminating redundancies and producing condensed information while maintaining consistency in collected documents. Automatic document summarization technology is to process a large amount of documents automatically and efficiently by extracting main sentences from a document using a computer and eliminating overlapped contents. To summarize a document efficiently, the present study uses a text rank algorithm. The text rank algorithm expresses sentences or keywords as a graph and employs the peaks or main lines of a graph to figure out semantic correlation between words and sentences and understand the importance of sentences. Through this, it goes through a process to extract the superordinate keywords of sentences and extract main sentences based on those keywords. To go through a process to extract main sentence s, word grouping is done. For word grouping, a particular weighting scale is used to screen sentences having a high weighted value. Based on the screen sentences, main sentences are extracted, and the document is summarized. This study has proven that it shows higher performance than any of the document summarization methods previously examined and performs summarization more efficiently.