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Found 9 papers, 8 papers with code
UniHD@CL-SciSumm 2020: Citation Extraction as Search
This work presents the entry by the team from Heidelberg University in the CL-SciSumm 2020 shared task at the Scholarly Document Processing workshop at EMNLP 2020.
BLESS: Benchmarking Large Language Models on Sentence Simplification
Our performance benchmark will be available as a resource for the development of future TS methods and evaluation metrics.
Evaluating Factual Consistency of Texts with Semantic Role Labeling
We introduce SRLScore, a reference-free evaluation metric designed with text summarization in mind.
On the State of German (Abstractive) Text Summarization
We attribute poor evaluation quality to a variety of different factors, which are investigated in more detail in this work: A lack of qualitative (and diverse) gold data considered for training, understudied (and untreated) positional biases in some of the existing datasets, and the lack of easily accessible and streamlined pre-processing strategies or analysis tools.
UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?
Previous state-of-the-art models for lexical simplification consist of complex pipelines with several components, each of which requires deep technical knowledge and fine-tuned interaction to achieve its full potential.
EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain
Existing summarization datasets come with two main drawbacks: (1) They tend to focus on overly exposed domains, such as news articles or wiki-like texts, and (2) are primarily monolingual, with few multilingual datasets.
Klexikon: A German Dataset for Joint Summarization and Simplification
Traditionally, Text Simplification is treated as a monolingual translation task where sentences between source texts and their simplified counterparts are aligned for training.
Ranked #1 on
Text Summarization
on Klexikon
BERT got a Date: Introducing Transformers to Temporal Tagging
By supplementing training resources with weakly labeled data from rule-based systems, our model surpasses previous works in temporal tagging and type classification, especially on rare classes.
Ranked #1 on
Temporal Tagging
on TempEval-3
Structural Text Segmentation of Legal Documents
The growing complexity of legal cases has lead to an increasing interest in legal information retrieval systems that can effectively satisfy user-specific information needs.
