activity
20172023
most citedStructural Text Segmentation of Legal Documents

36 citations · 80 across the 12 of their papers we have counts for

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Showing cs.CLShow all

10 papers · 1 filter

cs.CL2023★ 1 cited

Evaluating Factual Consistency of Texts with Semantic Role Labeling

Jing Fan, Dennis Aumiller, Michael Gertz

Automated evaluation of text generation systems has recently seen increasing attention, particularly checking whether generated text stays truthful to input sources. Existing metho…

cs.CL2023★ 1 cited

CQE: A Comprehensive Quantity Extractor

Satya Almasian, Vivian Kazakova, Philip Göldner +1

Quantities are essential in documents to describe factual information. They are ubiquitous in application domains such as finance, business, medicine, and science in general. Compa…

cs.CL2023

On the State of German (Abstractive) Text Summarization

Dennis Aumiller, Jing Fan, Michael Gertz

With recent advancements in the area of Natural Language Processing, the focus is slowly shifting from a purely English-centric view towards more language-specific solutions, inclu…

cs.CL2023★ 1 cited

UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?

Dennis Aumiller, Michael Gertz

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 int…

cs.CL2022★ 3 cited

EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain

Dennis Aumiller, Ashish Chouhan, Michael Gertz

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 mono…

cs.CL2022

Klexikon: A German Dataset for Joint Summarization and Simplification

Dennis Aumiller, Michael Gertz

Traditionally, Text Simplification is treated as a monolingual translation task where sentences between source texts and their simplified counterparts are aligned for training. How…