Papers with English news

11 papers
A Corpus for Sentence-Level Subjectivity Detection on English News Articles (2024.lrec-main)

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Challenge: Existing approaches to spotting subjectivity require language-specific tools.
Approach: They develop annotation guidelines for sentence-level subjectivity detection that are not limited to language-specific cues.
Outcome: The proposed framework enables subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation.
A Study on Scaling Up Multilingual News Framing Analysis (2024.findings-naacl)

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Challenge: Existing studies on media framing have focused on English only data, leaving a gap in research concerning multilingual contexts.
Approach: They propose to use crowd-sourced datasets to automate framing analysis by automating translation and annotation.
Outcome: The proposed system improves on existing models in Bengali and Portuguese . the proposed system can train on a crowd-sourced dataset in 12 languages .
CoBaLD Annotation: The Enrichment of the Enhanced Universal Dependencies with the Semantical Pattern (2024.lrec-main)

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Challenge: a new annotation format is developed to support morphological, syntactic and semantic markup . the format is based on the Compreno semantics, which is a simplified version of the standard .
Approach: They propose a new annotation format that combines Enhanced UD morphosyntax and Compreno semantic pattern to enrich the UD annotation with word meanings and labels for semantic relations between words.
Outcome: The proposed format is aimed at morphological, syntactic and especially semantic markup . the proposed format reduces the number of semantic fields denoting lexical meanings .
MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)

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Challenge: Personalized news recommendation is an important technique for personalized news service.
Approach: They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding .
Outcome: The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body.
Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing (2023.acl-long)

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Challenge: a growing body of work attempts to automatically detect media frames in the news or social media, but most adopts a topic-like view on frames, evading modelling the broader document-level narrative.
Approach: They propose an annotation paradigm that breaks a complex annotation task into a series of simple binary questions.
Outcome: The proposed method is both effective and transparent in its predictions.
Weakly Supervised Headline Dependency Parsing (2022.findings-emnlp)

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Challenge: English news headlines have unique syntactic properties documented in linguistics literature since the 1930s.
Approach: They propose to provide the first news headline corpus of annotated syntactic dependency trees to evaluate existing NLP parsers on news headlines.
Outcome: The proposed method improves performance across different news outlets, but is moderated by constructions idiosyncratic to outlet.
Cross-Register Projection for Headline Part of Speech Tagging (2021.emnlp-main)

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Challenge: Part of speech (POS) tagging models are underperforming on headlines due to differences in the register of English news headlines and long-form text.
Approach: They propose to annotate news headlines with POS tags by projecting predicted tags from corresponding sentences in news bodies.
Outcome: The proposed model reduces errors by 23% and 19% on a newly-annotated corpus of over 5,248 English news headlines from the Google sentence compression corpus.
Context in Informational Bias Detection (2020.coling-main)

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Challenge: Informational bias is conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers’ opinions towards entities.
Approach: They explore four kinds of context for informational bias in English news articles . integrating event context improves classification performance over a strong baseline .
Outcome: The best-performing model outperforms the baseline on longer sentences and sentences from politically centrist articles.
MLSUM: The Multilingual Summarization Corpus (2020.emnlp-main)

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Challenge: Existing biases in multi-lingual datasets are limiting the use of multilingual data in document summarization tasks.
Approach: They present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Outcome: The proposed dataset contains 1.5M+ article/summary pairs in five different languages.
HeadlineCause: A Dataset of News Headlines for Detecting Causalities (2022.lrec-1)

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Challenge: Existing datasets focus on commonsense causal reasoning or explicit causal relations . authors present dataset for detecting implicit causal relations between news headlines .
Approach: They present a dataset for detecting implicit causal relations between news headlines . they use 5000 headline pairs from English news and 9000 from Russian news .
Outcome: The proposed dataset shows that it is valid and can be used to predict implicit causal relations between headline pairs.
Multi-Label and Multilingual News Framing Analysis (2020.acl-main)

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Challenge: Recent studies have focused on news framing in English, but few studies have explored how it can be extended to other languages and in multi-label settings.
Approach: They propose a method that leverages dictionary and few annotations to detect frames from just the headline in a low-resource context.
Outcome: The proposed method performs better than translating the entire headline to the source language . it can be scaled up to many languages, even those without existing translation technologies .

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