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Found 84 papers, 50 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.
Image Retrieval for Arguments Using Stance-Aware Query Expansion
Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far.
On Classifying whether Two Texts are on the Same Side of an Argument
To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed.
Language Models as Context-sensitive Word Search Engines
Context-sensitive word search engines are writing assistants that support word choice, phrasing, and idiomatic language use by indexing large-scale n-gram collections and implementing a wildcard search.
CausalQA: A Benchmark for Causal Question Answering
At least 5% of questions submitted to search engines ask about cause-effect relationships in some way.
Mining Health-related Cause-Effect Statements with High Precision at Large Scale
An efficient assessment of the health relatedness of text passages is important to mine the web at scale to conduct health sociological analyses or to develop a health search engine.
Casting the Same Sentiment Classification Problem
We introduce and study a problem variant of sentiment analysis, namely the “same sentiment classification problem”, where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the actual sentiment polarity.
AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs
However, no tool currently allows end users to directly leverage LLMs for screening or facilitates systematic and transparent usage of LLM-assisted screening methods.
Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions
In a user study, our method correlates with the preferences of a human expert (Kendall's $\tau=0. 64$).
Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins
Representation-based retrieval models, so-called biencoders, estimate the relevance of a document to a query by calculating the similarity of their respective embeddings.
Systematic Evaluation of Neural Retrieval Models on the Touché 2020 Argument Retrieval Subset of BEIR
Our black-box evaluation reveals an inherent bias of neural models towards retrieving short passages from the Touch\'e 2020 data, and we also find that quite a few of the neural models' results are unjudged in the Touch\'e 2020 data.
Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking
Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data.
If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level
Trigger warnings are labels that preface documents with sensitive content if this content could be perceived as harmful by certain groups of readers.
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Existing cross-encoder models can be categorized as pointwise, pairwise, or listwise.
Task-Oriented Paraphrase Analytics
Since paraphrasing is an ill-defined task, the term "paraphrasing" covers text transformation tasks with different characteristics.
Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models
Modern sequence-to-sequence relevance models like monoT5 can effectively capture complex textual interactions between queries and documents through cross-encoding.
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.
Detecting Generated Native Ads in Conversational Search
In this paper, we thus take a first step to investigate whether LLMs can also be used as a countermeasure, i. e., to block generated native ads.
Zero-shot Generative Large Language Models for Systematic Review Screening Automation
Systematic reviews are crucial for evidence-based medicine as they comprehensively analyse published research findings on specific questions.
Evaluating Generative Ad Hoc Information Retrieval
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.
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.
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Our best approach is not only viable based on the information available at the time of screening, but also has similar effectiveness to the final title.
Manipulating Embeddings of Stable Diffusion Prompts
Prompt engineering is still the primary way for users of generative text-to-image models to manipulate generated images in a targeted way.
OpinionConv: Conversational Product Search with Grounded Opinions
This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products.
GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration
We discuss how Pyserini - a widely used toolkit for reproducible IR research can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts.
The Information Retrieval Experiment Platform
Standardization is achieved when a retrieval approach implements PyTerrier's interfaces and the input and output of an experiment are compatible with ir_datasets and ir_measures.
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.
Using Language Models on Low-end Hardware
This paper evaluates the viability of using fixed language models for training text classification networks on low-end hardware.
Perspectives on Large Language Models for Relevance Judgment
When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems.
The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives
The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years.
Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face
We present Spacerini, a tool that integrates the Pyserini toolkit for reproducible information retrieval research with Hugging Face to enable the seamless construction and deployment of interactive search engines.
Paraphrase Acquisition from Image Captions
We propose to use image captions from the Web as a previously underutilized resource for paraphrases (i. e., texts with the same "message") and to create and analyze a corresponding dataset.
Topic Ontologies for Arguments
Many computational argumentation tasks, like stance classification, are topic-dependent: the effectiveness of approaches to these tasks significantly depends on whether the approaches were trained on arguments from the same topics as those they are tested on.
The Infinite Index: Information Retrieval on Generative Text-To-Image Models
Conditional generative models such as DALL-E and Stable Diffusion generate images based on a user-defined text, the prompt.
SMAuC -- The Scientific Multi-Authorship Corpus
The rapidly growing volume of scientific publications offers an interesting challenge for research on methods for analyzing the authorship of documents with one or more authors.
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.
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.
Trigger Warnings: Bootstrapping a Violence Detector for FanFiction
We present the first dataset and evaluation results on a newly defined computational task of trigger warning assignment.
Sparse Pairwise Re-ranking with Pre-trained Transformers
Pairwise re-ranking models predict which of two documents is more relevant to a query and then aggregate a final ranking from such preferences.
How Train-Test Leakage Affects Zero-shot Retrieval
We investigate the impact of this unintended train-test leakage by training neural retrieval models on combinations of a fixed number of MS MARCO / ORCAS queries that are highly similar to the actual test queries and an increasing number of other queries.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Clickbait Spoiling via Question Answering and Passage Retrieval
We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post.
Tracking Discourse Influence in Darknet Forums
This technical report documents our efforts in addressing the tasks set forth by the 2021 AMoC (Advanced Modelling of Cyber Criminal Careers) Hackathon.
STEREO: Scientific Text Reuse in Open Access Publications
We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications.
FastWARC: Optimizing Large-Scale Web Archive Analytics
Web search and other large-scale web data analytics rely on processing archives of web pages stored in a standardized and efficient format.
The Impact of Main Content Extraction on Near-Duplicate Detection
Commercial web search engines employ near-duplicate detection to ensure that users see each relevant result only once, albeit the underlying web crawls typically include (near-)duplicates of many web pages.
BERTian Poetics: Constrained Composition with Masked LMs
Masked language models have recently been interpreted as energy-based sequence models that can be generated from using a Metropolis--Hastings sampler.
Modeling Proficiency with Implicit User Representations
We introduce the problem of proficiency modeling: Given a user's posts on a social media platform, the task is to identify the subset of posts or topics for which the user has some level of proficiency.
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.
Small-Text: Active Learning for Text Classification in Python
We introduce small-text, an easy-to-use active learning library, which offers pool-based active learning for single- and multi-label text classification in Python.
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers
Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings.
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.
Crawling and Preprocessing Mailing Lists At Scale for Dialog Analysis
This paper introduces the Webis Gmane Email Corpus 2019, the largest publicly available and fully preprocessed email corpus to date.
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.
Efficient Pairwise Annotation of Argument Quality
We present an efficient annotation framework for argument quality, a feature difficult to be measured reliably as per previous work.
The Importance of Suppressing Domain Style in Authorship Analysis
The prerequisite of many approaches to authorship analysis is a representation of writing style.
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
Common Conversational Community Prototype: Scholarly Conversational Assistant
This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions.
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.
Celebrity Profiling
Celebrities are among the most prolific users of social media, promoting their personas and rallying followers.
Bias Analysis and Mitigation in the Evaluation of Authorship Verification
The PAN series of shared tasks is well known for its continuous and high quality research in the field of digital text forensics.
Heuristic Authorship Obfuscation
Authorship verification is the task of determining whether two texts were written by the same author.
Generalizing Unmasking for Short Texts
Authorship verification is the problem of inferring whether two texts were written by the same author.
SemEval-2019 Task 4: Hyperpartisan News Detection
Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint.
The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
Clickbait has grown to become a nuisance to social media users and social media operators alike.
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.
CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
Crowdsourcing a Large Corpus of Clickbait on Twitter
To address the urging task of clickbait detection, we constructed a new corpus of 38, 517 annotated Twitter tweets, the Webis Clickbait Corpus 2017.
Heuristic Feature Selection for Clickbait Detection
We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017.
Building an Argument Search Engine for the Web
Computational argumentation is expected to play a critical role in the future of web search.
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.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
A Stylometric Inquiry into Hyperpartisan and Fake News
The articles originated from 9 well-known political publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the hyperpartisan right-wing.
