Klexikon: A German Dataset for Joint Summarization and Simplification

LREC 2022  ยท  Dennis Aumiller, Michael Gertz ยท

Traditionally, Text Simplification is treated as a monolingual translation task where sentences between source texts and their simplified counterparts are aligned for training. However, especially for longer input documents, summarizing the text (or dropping less relevant content altogether) plays an important role in the simplification process, which is currently not reflected in existing datasets. Simultaneously, resources for non-English languages are scarce in general and prohibitive for training new solutions. To tackle this problem, we pose core requirements for a system that can jointly summarize and simplify long source documents. We further describe the creation of a new dataset for joint Text Simplification and Summarization based on German Wikipedia and the German children's lexicon "Klexikon", consisting of almost 2900 documents. We release a document-aligned version that particularly highlights the summarization aspect, and provide statistical evidence that this resource is well suited to simplification as well. Code and data are available on Github: https://github.com/dennlinger/klexikon

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Datasets


Introduced in the Paper:

Klexikon

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Text Summarization Klexikon Luhn's algorithm (25 sentences) ROUGE-1 32.00 # 1
ROUGE-2 5.63 # 1
ROUGE-L 11.68 # 2
Text Summarization Klexikon Full article ROUGE-1 16.98 # 4
ROUGE-2 4.30 # 3
ROUGE-L 7.09 # 4
Text Summarization Klexikon Lead-k ROUGE-1 25.00 # 2
ROUGE-2 5.16 # 2
ROUGE-L 12.10 # 1
Text Summarization Klexikon Lead-3 ROUGE-1 17.50 # 3
ROUGE-2 3.94 # 4
ROUGE-L 9.99 # 3

Methods


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