Papers with news domain
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining (2021.emnlp-main)
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| Challenge: | Existing methods for low-resource dialogue summarization neglect the difference between dialogues and conventional articles. |
| Approach: | They propose a multi-source pretraining paradigm to leverage external summary data . they exploit large-scale in-domain non-summary data to separate dialogue encoder and summary decoder . |
| Outcome: | The proposed model can be used to better leverage external summary data. |
Echoes from Alexandria: A Large Resource for Multilingual Book Summarization (2023.findings-acl)
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| Challenge: | Recent research in text summarization has focused on news stories, where texts are typically short and have strong layout features. |
| Approach: | They propose a resource for multilingual book summarization that uses a new extractive-then-abstractive baseline to compare the results. |
| Outcome: | The proposed resource is the largest and first to be multilingual, featuring 5 languages and 25 language pairs. |
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource (N18-1)
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| Challenge: | Existing temporal extraction systems that extract temporal relations can be improved by using a resource that provides prior knowledge of the temporal order that events usually follow. |
| Approach: | They propose to use a probabilistic knowledge base acquired in the news domain to extract temporal relations between events from the New York Times articles over a 20-year span. |
| Outcome: | The proposed system and resource are both publicly available. |
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization (2020.findings-emnlp)
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| Challenge: | Recent advances in text summarization have overcome position bias in news articles . however, there are long-standing, unresolved challenges in extractive summarizing . |
| Approach: | They propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions. |
| Outcome: | The proposed framework can flexibly control summary generation by introducing sub-aspect functions . extracted summaries with minimal position bias are comparable with standard models . |
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem (2022.findings-acl)
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| Challenge: | Existing methods for entity linking do not use a knowledge base or candidate sets. |
| Approach: | They propose an autoregressive entity linking model that is trained with two auxiliary tasks and learns to re-rank generated samples at inference time. |
| Outcome: | The proposed model improves on two biomedical datasets and a news domain dataset without the use of a knowledge base or candidate sets. |
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (P19-1)
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| Challenge: | Existing text summarization datasets are compiled from news articles, where summary-worthy content often appears in the beginning of input articles. |
| Approach: | They present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. |
| Outcome: | The proposed dataset is compared with existing summarization datasets and demonstrates that salient content is evenly distributed in the input. |
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application (2021.findings-emnlp)
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| Challenge: | Existing language models are pre-trained and distilled on general corpus like Wikipedia, which has gaps with the news domain and may be suboptimal for news intelligence. |
| Approach: | They propose a method to distill existing language models on Wikipedia to enable efficient news intelligence. |
| Outcome: | The proposed model can be used to build and test a news intelligence application on Wikipedia and Wikipedia. |
EDIS: Entity-Driven Image Search over Multimodal Web Content (2023.emnlp-main)
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| Challenge: | Existing image retrieval methods require large datasets and a large candidate set. |
| Approach: | They propose a news-domain dataset for cross-modal image search with 1 million web images . they propose combining multimodal image-text pairs with a million candidates . |
| Outcome: | The proposed dataset challenges state-of-the-art methods with dense entities and the large-scale candidate set. |
Annotated Corpus for Sentiment Analysis in Odia Language (2020.lrec-1)
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| Challenge: | Existing sentiment analysis models are not available for Odia 1 as it is a resource-poor language. |
| Approach: | They create an annotated Odia corpus and test its usability by training and testing on the corpus using various classifiers. |
| Outcome: | The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme. |
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation (2022.emnlp-main)
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| Challenge: | Existing work fine tunes the PLM with the news recommendation task, which can cause a domain shift problem. |
| Approach: | They propose a self-supervised method to adapt general PLM to news domain with a contrastive matching task between news titles and news bodies. |
| Outcome: | The proposed method can improve both the effectiveness and efficiency of the large PLM-based news recommendation model while maintaining its performance. |
The State and Fate of Summarization Datasets: A Survey (2025.naacl-long)
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| Challenge: | Summarization is the task of shortening a text while preserving the most important information it contains. |
| Approach: | They propose a novel ontology covering sample properties, collection methods and distribution covering sample characteristics, collection method and distribution. |
| Outcome: | The proposed ontology covers sample properties, collection methods and distribution, and can be used to streamline future research into a more coherent body of work. |
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge (2022.emnlp-main)
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Revanth Gangi Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi Fung, Kathryn Conger, Ahmed ELsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji
| Challenge: | Current claims detection methods focus on sentence analysis, ignoring other attributes . a key element of identifying misinformation is detecting the claims and the arguments that have been presented. |
| Approach: | They propose a benchmark for attribute-aware claim detection in the news domain . they extend the problem to include extraction of additional attributes related to each claim . |
| Outcome: | The proposed system performs well on the test, but human performance is still poor. |
MAD-TSC: A Multilingual Aligned News Dataset for Target-dependent Sentiment Classification (2023.acl-long)
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| Challenge: | Sentiment classification is a task that requires domain-specific datasets. |
| Approach: | They propose a new dataset which includes aligned examples in eight languages . they show that machine translations can replace manual ones and that results match English . |
| Outcome: | The proposed dataset compares the performance of the proposed model with existing datasets in eight languages and human and machine translations. |
EDEN: A Dataset for Event Detection in Norwegian News (2024.lrec-main)
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Samia Touileb, Jeanett Murstad, Petter Mæhlum, Lubos Steskal, Lilja Charlotte Storset, Huiling You, Lilja Øvrelid
| Challenge: | EDEN is the first dataset annotated with event information at the sentence level for the Norwegian language. |
| Approach: | They propose to annotate Norwegian news text and transcribed speech using ACE event schema. |
| Outcome: | The proposed dataset is the first annotated dataset for Norwegian, with a language-specific annotation process. |
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents (2020.coling-main)
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| Challenge: | Existing studies suggest that Neural Machine Translation still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. |
| Approach: | They propose to evaluate the robustness of Neural Machine Translation models against specific linguistic phenomena in Japanese-English translation. |
| Outcome: | The proposed model can handle user-generated content (UGC) on the Internet, but it is difficult to translate clean inputs. |
KC4MT: A High-Quality Corpus for Multilingual Machine Translation (2022.lrec-1)
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Vinh Van Nguyen, Ha Nguyen, Huong Thanh Le, Thai Phuong Nguyen, Tan Van Bui, Luan Nghia Pham, Anh Tuan Phan, Cong Hoang-Minh Nguyen, Viet Hong Tran, Anh Huu Tran
| Challenge: | In machine translation, Vietnamese is a low-resource language, and the quality of the training corpus is very low. |
| Approach: | They propose a method for building high-quality multilingual parallel corpus in news domain . they also publicize a corpus that includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
| Outcome: | The proposed method improves the quality of multilingual machine translation in Vietnamese, Laos, and Khmer . the public version includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains (2024.emnlp-main)
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Yang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, Yilun Zhu, Shabnam Behzad, Lauren Levine, Jessica Lin, Devika Tiwari, Amir Zeldes
| Challenge: | Existing shallow discourse parsing systems focus on the Wall Street Journal corpus, but the data is limited to the news domain and is 35 years old. |
| Approach: | They propose to use the Wall Street Journal corpus as a benchmark for PDTB-style shallow discourse parsing. |
| Outcome: | The proposed dataset is compatible with PDTB, but suffers from degradation out-of-domain. |
Learning to Generate Overlap Summaries through Noisy Synthetic Data (2022.emnlp-main)
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| Challenge: | Existing training data for seq-to-seq models is limited due to the lack of available training data. |
| Approach: | They propose a data augmentation technique which allows to create large amount of synthetic data for training a seq-to-seq model. |
| Outcome: | The proposed technique performs better than pre-trained models on news domains and is close to the existing methods on golden training data. |
GameWikiSum: a Novel Large Multi-Document Summarization Dataset (2020.lrec-1)
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| Challenge: | Existing datasets contain only hundreds of samples, resulting in heavy reliance on hand-crafted features or manually annotated data. |
| Approach: | They propose a new domain-specific dataset for multi-document summarization that is 100 times larger than commonly used datasets. |
| Outcome: | The proposed dataset is 100 times larger than commonly used datasets and in another domain than news. |