Abstract
We studied ways of extracting important information from natural language processing article abstracts. While many kinds of information extraction, such as from newspapers, web materials, or biology article abstracts, already exist, no papers have been written about extracting natural language processing information. We hope that this study will be useful for natural language processing researchers. We defined nine categories that contain important expressions for natural language processing article abstracts. We constructed a method of extracting these expressions by using machine learning methods. Our method extracted these expressions with an F-measure of 0.67. When we considered partially correct expressions to be correct, the F-measure increased to 0.73. In particular, important expressions belonging to four categories (Accuracy, Field, Language, and Name) were automatically extracted at a high F-measure (about 0.8 to 0.9) using our method. We next constructed various kinds of visualization tools using important extracted expression. They can display the abstract of a paper highlighting extracted important expressions with color, display abstracts in a table including extracted inpotant expressions in each row, and make a graph indicating the distribution and trend of papers including important expressions for each category.
