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Found 15 papers, 6 papers with code
Task Proposal: Abstractive Snippet Generation for Web Pages
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.
Analyzing Culture-Specific Argument Structures in Learner Essays
Based on the ICLE corpus containing essays written by English learners of 16 different mother tongues, we train natural language processing models to mine argumentative discourse units (ADUs) as well as to assess the essays’ quality in terms of organization and argument strength.
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.
Belief-based Generation of Argumentative Claims
In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs.
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.
Abstractive Snippet Generation
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
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition
Many discussions on online platforms suffer from users offending others by using abusive terminology, threatening each other, or being sarcastic.
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.
Unit Segmentation of Argumentative Texts
The segmentation of an argumentative text into argument units and their non-argumentative counterparts is the first step in identifying the argumentative structure of the text.
UTCNN: a Deep Learning Model of Stance Classification on Social Media Text
Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0. 755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld.
Chinese Textual Sentiment Analysis: Datasets, Resources and Tools
The basic processing tools are from CKIP Participants can download these resources, use them and solve the problems they encounter in this tutorial.
WordForce: Visualizing Controversial Words in Debates
This paper presents WordForce, a system powered by the state of the art neural network model to visualize the learned user-dependent word embeddings from each post according to the post content and its engaged users.
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text
Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0. 755 macro-average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld.
