Feature Extraction and Summarization of Recipes Using Flow Graph

  • Yoko Yamakata
  • Shinji Imahori
  • Yuichi Sugiyama
  • Shinsuke Mori
  • Katsumi Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8238)


These days, there are more than a million recipes on the Web. When you search for a recipe with one query such as “nikujaga,” the name of a typical Japanese food, you can find thousands of “nikujaga” recipes as the result. Even if you focus on only the top ten results, it is still difficult to find out the characteristic feature of each recipe because a cooking is a work-flow including parallel procedures. According to our survey, people place the most importance on the differences of cooking procedures when they compare the recipes. However, such differences are difficult to be extracted just by comparing the recipe texts as existing methods. Therefore, our system extracts (i) a general way to cook as a summary of cooking procedures and (ii) the characteristic features of each recipe by analyzing the work-flows of the top ten results. In the experiments, our method succeeded in extracting 54% of manually extracted features while the previous research addressed 37% of them.


Edit Distance Flow Graph Name Entity Recognition Edit Operation Name Entity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Yoko Yamakata
    • 1
  • Shinji Imahori
    • 2
  • Yuichi Sugiyama
    • 1
  • Shinsuke Mori
    • 3
  • Katsumi Tanaka
    • 1
  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan
  2. 2.Graduate School of EngineeringNagoya UniversityNagoyaJapan
  3. 3.Academic Center for Computing and Media StudiesKyoto UniversityKyotoJapan

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