The web is full of guidance on a wide variety of tasks, from changing the oil in your car to baking an apple pie. However, as content is created independently, a single task could have thousands of corresponding procedural texts. This makes it difficult for users to view the bigger picture and understand the multiple ways the task could be accomplished. In this work we propose an unsupervised learning approach for summarizing multiple procedural texts into an intuitive graph representation, allowing users to easily explore commonalities and differences. We demonstrate our approach on recipes, a prominent example of procedural texts. User studies show that our representation is intuitive and coherent and that it has the potential to help users with several sensemaking tasks, including adapting recipes for a novice cook and finding creative ways to spice up a dish.
|Original language||American English|
|Title of host publication||CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||11|
|State||Published - 26 Oct 2021|
|Event||30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia|
Duration: 1 Nov 2021 → 5 Nov 2021
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||30th ACM International Conference on Information and Knowledge Management, CIKM 2021|
|Period||1/11/21 → 5/11/21|
Bibliographical noteFunding Information:
We thank the anonymous reviewers for their insightful comments, Hyadata Lab members for thoughtful remarks, and the participants in our user studies. This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 852686, SIAM).
© 2021 ACM.
- cooking recipes
- multi-document summarization
- procedural texts