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Klexikon: A German Dataset for Joint Summarization and Simplification

2022-01-18LREC 2022Code Available1· sign in to hype

Dennis Aumiller, Michael Gertz

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Abstract

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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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
KlexikonLuhn's algorithm (25 sentences)ROUGE-132Unverified
KlexikonLead-kROUGE-125Unverified
KlexikonLead-3ROUGE-117.5Unverified
KlexikonFull articleROUGE-116.98Unverified

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