Papers with headline generation
Shironaam: Bengali News Headline Generation using Auxiliary Information (2023.eacl-main)
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| Challenge: | Automated headline generation systems have the potential to assist editors in finding interesting headlines to attract visitors or readers. |
| Approach: | They propose to use Bengali news article-headline pairings with auxiliary data to better model headline generation using pre-trained language models. |
| Outcome: | The proposed model improves on a Bengali news headline generation dataset by 3 to 10 percentage points over baselines. |
Towards Unified Uni- and Multi-modal News Headline Generation (2024.findings-eacl)
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| Challenge: | Current approaches to multimodal summarization and headline generation are limited by hierarchical cross-modal encoders and modality-specific decoders. |
| Approach: | They propose a task formulation that utilizes a simple encoder-decoder model to generate headlines from uni- and multimodal news articles. |
| Outcome: | The proposed model is trained on data of several modalities and extends the decoder to handle the multimodal output. |
PrahokBART: A Pre-trained Sequence-to-Sequence Model for Khmer Natural Language Generation (2025.coling-main)
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| Challenge: | Pre-trained sequence-to-sequence models are typically pretrained on extensive raw text corpora and fine-tuned on task-specific data. |
| Approach: | They introduce a pre-trained sequence-to-sequence model trained from scratch for Khmer using carefully curated Khmer and English corpora. |
| Outcome: | The proposed model outperforms existing models on three generative tasks and is data-efficient and effective in enhancing performance across various natural language generation tasks. |
DivHSK: Diverse Headline Generation using Self-Attention based Keyword Selection (2023.findings-acl)
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| Challenge: | Diverse headline generation is an NLP task where the goal is to generate multiple headlines that are true to the content of the article but are different among themselves. |
| Approach: | They propose a novel model that generates multiple diverse headlines by using a pre-trained encoder and a cluster-specific keyword set. |
| Outcome: | The proposed model outperforms existing literature and their strong baselines and emerges as a state-of-the-art model. |
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. |
Varta: A Large-Scale Headline-Generation Dataset for Indic Languages (2023.findings-acl)
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| Challenge: | Varta dataset includes more than 41 million pairs of headlines and articles in 14 different Indic languages (and English) |
| Approach: | They present a large-scale multilingual dataset for headline generation in Indic languages. |
| Outcome: | The Varta dataset includes more than 41 million pairs of headlines and articles in 14 different Indic languages (and English) the data can be used to train strong language models that outperform competitive baselines in both NLU and NLG benchmarks. |
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. |
STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents (2022.lrec-1)
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| Challenge: | Formal documents are organized into sections of text, each with a title . but there is no corpus of web documents annotated with titles and prose texts . cnn.com's john mccarthy and daniel mclears are working on a new title-text dataset . |
| Approach: | They propose a first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training. |
| Outcome: | The proposed system outperforms baseline models in terms of title-text identification. |
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings (2022.lrec-1)
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| Challenge: | Neural approaches for natural language generation (NLG) have mushroomed due to large textual resources. |
| Approach: | They propose to use a pretrained multilingual encoder-decoder model and a combination of two pretrained language models to train a model in a low-resource setting. |
| Outcome: | The proposed model outperforms the previous model on English and on a small subset of the same data. |
Question Answering as an Automatic Evaluation Metric for News Article Summarization (N19-1)
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| Challenge: | Recent work on summarization and headline generation focuses on maximizing ROUGE scores. |
| Approach: | They propose an extrinsic evaluation metric that maximizes ROUGE scores for automatic summarization and headline generation. |
| Outcome: | The proposed model maximizes ROUGE scores while increasing competitive results. |
COSMMIC: Comment-Sensitive Multimodal Multilingual Indian Corpus for Summarization and Headline Generation (2025.acl-long)
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Raghvendra Kumar, Mohammed Salman S A, Aryan Sahu, Tridib Nandi, Pragathi Y P, Sriparna Saha, Jose G Moreno
| Challenge: | COSMMIC is a multimodal, multilingual dataset featuring nine major Indian languages. |
| Approach: | They propose a multimodal, multilingual multimodal multimodal dataset that integrates text, images and user feedback to enhance summarization. |
| Outcome: | The proposed dataset is based on 4,959 article-image pairs and 24,484 reader comments with ground-truth summaries available in all included languages. |
Updated Headline Generation: Creating Updated Summaries for Evolving News Stories (2022.acl-long)
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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. |
Direct Output Connection for a High-Rank Language Model (D18-1)
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| Challenge: | Neural network language models have played a central role in recent natural language processing advances. |
| Approach: | They propose a recurrent neural network language model that combines probability distributions computed from a final RNN layer and also middle layers. |
| Outcome: | The proposed language model achieves the best score on the Penn Treebank and WikiText-2 benchmark datasets. |
Language Modeling with Sparse Product of Sememe Experts (D18-1)
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| Challenge: | Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words. |
| Approach: | They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics . |
| Outcome: | Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM. |
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 . |
StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation (2023.findings-emnlp)
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| Challenge: | Existing studies on unsupervised headline generation focus on a standard dataset and mono-style corpora. |
| Approach: | They propose an unsupervised approach for stylistic headline generation using a pretrained BART model decorated with adapters responsible for different styles. |
| Outcome: | The proposed method separates the task of style learning and headline generation, allowing for the generation of diverse headlines with diverse styles. |
XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags (2024.findings-acl)
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| Challenge: | XL-HeadTags is a dataset that includes 20 languages across 6 diverse language families. |
| Approach: | They propose to leverage auxiliary information such as images and captions embedded in news articles to retrieve relevant sentences and utilize instruction tuning with variations to generate both headlines and tags for news articles in a multilingual context. |
| Outcome: | The proposed approach generates headlines and tags in a multilingual context using images and captions embedded in the articles and instruction tuning with variations. |