# Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps

arXiv: https://arxiv.org/abs/1704.04452 · HTML: https://mlsanity.com/paper/1704.04452/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2017.

## Built on by (most cited first, 3 of 3)

- [Indian Legal NLP Benchmarks : A Survey](https://mlsanity.com/paper/2107.06056/) (2021)
- [Weakly Supervised Concept Map Generation through Task-Guided Graph Translation](https://mlsanity.com/paper/2110.15720/) (2021)
- [Summarization with Graphical Elements](https://mlsanity.com/paper/2204.07551/) (2022)

## Abstract (arXiv)

Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multi-document summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.