We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization

lib:a6034244888e3f10 (v1.0.0)

Vote to reproduce this paper and share portable workflows   1 
Authors: Tobias Falke,Iryna Gurevych
Where published: NAACL 2019 6
Document:  PDF  DOI 
Artifact development version: GitHub
Abstract URL: https://www.aclweb.org/anthology/N19-1074/


Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries. As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. However, while the current state-of-the-art method suggested a new grouping method that was shown to improve the summary quality, its use of pairwise comparisons leads to polynomial runtime complexity that prohibits the application to large document collections. In this paper, we propose two alternative grouping techniques based on locality sensitive hashing, approximate nearest neighbor search and a fast clustering algorithm. They exhibit linear and log-linear runtime complexity, making them much more scalable. We report experimental results that confirm the improved runtime behavior while also showing that the quality of the summary concept maps remains comparable.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives

Comments  

Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!