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Found 20 papers, 11 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.
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.
Evaluating Generative Ad Hoc Information Retrieval
Our analysis provides a foundation and new insights for the evaluation of generative retrieval systems, focusing on ad hoc retrieval.
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.
Modeling Appropriate Language in Argumentation
Online discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility.
Summary Workbench: Unifying Application and Evaluation of Text Summarization Models
This paper presents Summary Workbench, a new tool for developing and evaluating text summarization models.
Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
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.
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.
Argument Undermining: Counter-Argument Generation by Attacking Weak Premises
We hypothesize that identifying the argument's weak premises is key to effective countering.
News Editorials: Towards Summarizing Long Argumentative Texts
With Webis-EditorialSum-2020, we present a corpus of 1330 carefully curated summaries for 266 news editorials.
Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness
Predicting the persuasiveness of arguments has applications as diverse as writing assistance, essay scoring, and advertising.
Target Inference in Argument Conclusion Generation
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.
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
Towards Summarization for Social Media - Results of the TL;DR Challenge
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.
Task Proposal: The TL;DR Challenge
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.
Retrieval of the Best Counterargument without Prior Topic Knowledge
Given any argument on any controversial topic, how to counter it?
TL;DR: Mining Reddit to Learn Automatic Summarization
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.
