Search Results for author:
Found 20 papers, 7 papers with code
TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization
This paper presents TL;DR Progress, a new tool for exploring the literature on neural text summarization.
Citance-Contextualized Summarization of Scientific Papers
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").
Indicative Summarization of Long Discussions
Online forums encourage the exchange and discussion of different stances on many topics.
Differential Bias: On the Perceptibility of Stance Imbalance in Argumentation
We see the problem not in the complexity of interpreting language phenomena but in the diversity of sociocultural backgrounds of the readers, which cannot be handled uniformly: To decide whether a text has crossed the proverbial line between non-biased and biased is subjective.
Controlled Neural Sentence-Level Reframing of News Articles
Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques.
Summary Explorer: Visualizing the State of the Art in Text Summarization
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.
Generating Informative Conclusions for Argumentative Texts
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.
Detecting Media Bias in News Articles using Gaussian Bias Distributions
In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model.
Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity
Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society.
Learning to Flip the Bias of News Headlines
This paper introduces the task of {``}flipping{''} the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias.
Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus
Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly.
Argumentation Synthesis following Rhetorical Strategies
In this paper, we model rhetorical strategies for the computational synthesis of effective argumentation.
Modeling Deliberative Argumentation Strategies on Wikipedia
This paper studies how the argumentation strategies of participants in deliberative discussions can be supported computationally.
Patterns of Argumentation Strategies across Topics
This paper presents an analysis of argumentation strategies in news editorials within and across topics.
Building an Argument Search Engine for the Web
Computational argumentation is expected to play a critical role in the future of web search.
WAT-SL: A Customizable Web Annotation Tool for Segment Labeling
A frequent type of annotations in text corpora are labeled text segments.
Using Argument Mining to Assess the Argumentation Quality of Essays
In particular, we investigate to what extent the mined structure can be leveraged to assess the argumentation quality of persuasive essays.
A News Editorial Corpus for Mining Argumentation Strategies
Many argumentative texts, and news editorials in particular, follow a specific strategy to persuade their readers of some opinion or attitude.
