no code implementations • INLG (ACL) 2020 • Shahbaz Syed, Wei-Fan Chen, Matthias Hagen, Benno Stein, Henning Wachsmuth, Martin Potthast
We propose a shared task on abstractive snippet generation for web pages, a novel task of generating query-biased abstractive summaries for documents that are to be shown on a search results page.
1 code implementation • 10 Feb 2024 • Shahbaz Syed, Khalid Al-Khatib, Martin Potthast
This paper presents TL;DR Progress, a new tool for exploring the literature on neural text summarization.
no code implementations • 8 Nov 2023 • Lukas Gienapp, Harrisen Scells, Niklas Deckers, Janek Bevendorff, Shuai Wang, Johannes Kiesel, Shahbaz Syed, Maik Fröbe, Guido Zuccon, Benno Stein, Matthias Hagen, Martin Potthast
To lay a foundation for developing new evaluation methods for generative retrieval systems, we survey the relevant literature from the fields of information retrieval and natural language processing, identify search tasks and system architectures in generative retrieval, develop a new user model, and study its operationalization.
1 code implementation • 4 Nov 2023 • Shahbaz Syed, Ahmad Dawar Hakimi, Khalid Al-Khatib, Martin Potthast
We propose a new contextualized summarization approach that can generate an informative summary conditioned on a given sentence containing the citation of a reference (a so-called "citance").
1 code implementation • 3 Nov 2023 • Shahbaz Syed, Dominik Schwabe, Khalid Al-Khatib, Martin Potthast
Online forums encourage the exchange and discussion of different stances on many topics.
1 code implementation • 24 May 2023 • Timon Ziegenbein, Shahbaz Syed, Felix Lange, Martin Potthast, Henning Wachsmuth
Online discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility.
1 code implementation • 18 Oct 2022 • Shahbaz Syed, Dominik Schwabe, Martin Potthast
This paper presents Summary Workbench, a new tool for developing and evaluating text summarization models.
1 code implementation • EMNLP (ArgMining) 2021 • Milad Alshomary, Timon Gurcke, Shahbaz Syed, Philipp Heinrich, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, Henning Wachsmuth
Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments.
1 code implementation • EMNLP (ACL) 2021 • Shahbaz Syed, Tariq Yousef, Khalid Al-Khatib, Stefan Jänicke, Martin Potthast
This paper introduces Summary Explorer, a new tool to support the manual inspection of text summarization systems by compiling the outputs of 55~state-of-the-art single document summarization approaches on three benchmark datasets, and visually exploring them during a qualitative assessment.
1 code implementation • Findings (ACL) 2021 • Shahbaz Syed, Khalid Al-Khatib, Milad Alshomary, Henning Wachsmuth, Martin Potthast
Third, insights are provided into the suitability of our corpus for the task, the differences between the two generation paradigms, the trade-off between informativeness and conciseness, and the impact of encoding argumentative knowledge.
1 code implementation • 25 May 2021 • Milad Alshomary, Shahbaz Syed, Arkajit Dhar, Martin Potthast, Henning Wachsmuth
We hypothesize that identifying the argument's weak premises is key to effective countering.
no code implementations • COLING 2020 • Shahbaz Syed, Roxanne El Baff, Johannes Kiesel, Khalid Al Khatib, Benno Stein, Martin Potthast
With Webis-EditorialSum-2020, we present a corpus of 1330 carefully curated summaries for 266 news editorials.
no code implementations • ACL 2020 • Khalid Al Khatib, Michael V{\"o}lske, Shahbaz Syed, Nikolay Kolyada, Benno Stein
Predicting the persuasiveness of arguments has applications as diverse as writing assistance, essay scoring, and advertising.
no code implementations • ACL 2020 • Milad Alshomary, Shahbaz Syed, Martin Potthast, Henning Wachsmuth
In particular, we argue here that a decisive step is to infer a conclusion{'}s target, and we hypothesize that this target is related to the premises{'} targets.
1 code implementation • 25 Feb 2020 • Wei-Fan Chen, Shahbaz Syed, Benno Stein, Matthias Hagen, Martin Potthast
An abstractive snippet is an originally created piece of text to summarize a web page on a search engine results page.
Ranked #1 on Text Summarization on Webis-Snippet-20 Corpus
no code implementations • WS 2019 • Shahbaz Syed, Michael V{\"o}lske, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze, Martin Potthast
In this paper, we report on the results of the TL;DR challenge, discussing an extensive manual evaluation of the expected properties of a good summary based on analyzing the comments provided by human annotators.
no code implementations • WS 2018 • Shahbaz Syed, Michael V{\"o}lske, Martin Potthast, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze
The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far.
no code implementations • ACL 2018 • Henning Wachsmuth, Shahbaz Syed, Benno Stein
Given any argument on any controversial topic, how to counter it?
no code implementations • WS 2017 • Michael V{\"o}lske, Martin Potthast, Shahbaz Syed, Benno Stein
Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data.