| Challenge: | a lack of fine-grained annotations for accurate numeral generation in headlines is a major roadblock . a new dataset, the NumHG, provides over 27,000 annotated numeral-rich news articles for detailed investigation . |
| Approach: | They propose a dataset that provides annotated numerals for headline generation . they evaluate five well-performing headline-generation models using human evaluation . |
| Outcome: | The proposed dataset provides annotated numeral-rich news articles for detailed investigation. |
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| Challenge: | Existing systems that generate headlines for updated articles are not as efficient as static ones. |
| Approach: | They propose a task where a system generates a headline for an updated article, considering both the previous article and headline. |
| Outcome: | The proposed model produces headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. |
A Case Study on Neural Headline Generation for Editing Support (N19-2)
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Kazuma Murao, Ken Kobayashi, Hayato Kobayashi, Taichi Yatsuka, Takeshi Masuyama, Tatsuru Higurashi, Yoshimune Tabuchi
| Challenge: | a news-aggregator is a website or mobile application that aggregates web content . dozens of professional editors manually create their headlines, which are much shorter than the original headlines. |
| Approach: | They propose a neural headline generation model that automatically generates short headlines from news articles. |
| Outcome: | The proposed model is deployed to an editing support tool and compares editors' behavior before and after the release. |
Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings (2020.lrec-1)
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| Challenge: | Recent generative language models have shown promise in abstractive summarization tasks. |
| Approach: | They propose to use Fr echet embedding distance and angular embeddable similarity to evaluate the performance of generative language models in abstractive summarization tasks. |
| Outcome: | The proposed metric shows close relation with human judgments and has overall better correlations with them. |
Improving Truthfulness of Headline Generation (2020.acl-main)
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| Challenge: | Existing studies on abstractive summarization report ROUGE scores, but are concerned about the truthfulness of generated summaries. |
| Approach: | They propose to remove untruthful instances from supervision data to improve headline generation . they build a binary classifier that predicts an entailment relation between an article and its headline . |
| Outcome: | The proposed model improves on two popular datasets. |
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2020.acl-main)
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| Challenge: | Current summarization systems only produce plain, factual headlines, far from the practical needs for exposure and memorableness of the articles. |
| Approach: | They propose a task to generate relevant headlines with three style options . they propose combining summarization and reconstruction tasks into a multitasking framework . |
| Outcome: | The proposed method outperforms the state-of-the-art summarization model by 9.68% . it can generate relevant, fluent headlines with humor, romance and clickbait . |
TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu (2024.lrec-main)
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| Challenge: | Relevance-based headline classification is under-explored in low-resource languages like Telugu due to a lack of annotated data. |
| Approach: | They propose that relevance-based headline classification can greatly aid the task of generating relevant headlines. |
| Outcome: | The proposed model can generate relevant headlines with 78,534 annotations in Telugu . the model shows a 5 point increment in the ROUGE-L scores . |
News Headline Grouping as a Challenging NLU Task (2021.naacl-main)
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| Challenge: | Recent advances in Natural Language Understanding (NLU) have seen models outperform human performance on many standard tasks. |
| Approach: | They propose a task of HeadLine Grouping and a dataset consisting of 20,056 pairs of news headlines, each labeled with a binary judgement as to whether the pair belongs within the same group. |
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CMHG: A Dataset and Benchmark for Headline Generation of Minority Languages in China (2025.emnlp-main)
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| Challenge: | Minority languages in China face significant challenges due to their unique writing systems, which differ from international standards. |
| Approach: | They propose a dataset specifically curated for headline generation tasks for minority languages in China . they propose 50,000 entries each for Uyghur and Mongolian, and a test set annotated by native speakers . |
| Outcome: | The proposed dataset will help improve headline generation in minority languages . it includes 100,000 entries for Tibetan, 50,000 entries each for Uyghur and Mongolian . |
Contrastive Learning enhanced Author-Style Headline Generation (2022.emnlp-main)
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| Challenge: | Current work only uses the article itself in the headline generation, but have not taken the writing style of headlines into account. |
| Approach: | They propose a model which takes historical headlines into account to integrate the stylistic features of the author into the model and integrate them into the decoder. |
| Outcome: | The proposed model can integrate the stylistic features of the author into the model and generate a headline that is appropriate for the article and consistent with the author’s style. |
Headline Token-based Discriminative Learning for Subheading Generation in News Article (2023.findings-eacl)
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| Challenge: | Existing models that generate news subheadings rely on topical headline information to capture topical knowledge from the article. |
| Approach: | They propose a model that uses topical headline information to generate news subheadings using masked headline tokens. |
| Outcome: | The proposed model outperforms the comparative models on three news datasets written in two languages and performs robustly on a small dataset and various masking ratios. |