Towards Multi-paper Summarization Using Reference Information
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Natural Language Processing. Pages 926-930.
Abstract
This paper presents a system to support writing a survey of a specific domain. The system utilizes reference information that consists of reference relationships between papers and the information derived from the description around citations. We think the following are inevitable for writing a survey : collecting papers of the specific domain, and understanding their essence and differences among them. Therefore, we firstly extract fragments of papers where the author describes the essence of a referred paper and the differences between his paper and it(we call them reference areas). Then with the information of reference areas, we identify the types of reference relationships that indicate the reasons for citations(we call them reference types). These types make it possible to collect papers in the same domain. The system can display the collection of the papers. It can also show abstracts and reference areas of the collected papers. With the system, we can understand the relationships between the collected papers.
@inproceedings{ijcai1999p132,
title = {Towards Multi-paper Summarization Using Reference Information},
author = {Nanba, Hidetsugu and Okumura, Manabu},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {926--930},
year = {1999},
note = {Natural Language Processing},
url = {https://ijcai.org/proceedings/1999/132},
}
TY - CPAPER TI - Towards Multi-paper Summarization Using Reference Information AU - Nanba, Hidetsugu AU - Okumura, Manabu T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 926 EP - 930 UR - https://ijcai.org/proceedings/1999/132 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: