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.

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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 .
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.
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.
A Case Study on Neural Headline Generation for Editing Support (N19-2)

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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.
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.
MediaHG: Rethinking Eye-catchy Features in Social Media Headline Generation (2023.emnlp-main)

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Challenge: Creating a good headline on social media platforms requires a disentanglement-based model to balance the content and contextual features.
Approach: They propose a disentanglement-based headline generation model which can balance the content and contextual features by incorporating contrastive learning and auxiliary multi-tasking to choose the best domain-suitable headline.
Outcome: The proposed model can balance content and contextual features, while allowing bloggers to obtain more site traffic and profits while readers can have easier access to topics of interest.
NumHG: A Dataset for Number-Focused Headline Generation (2024.lrec-main)

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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.
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation (2020.emnlp-main)

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Challenge: Existing methods for news headline generation focus on producing a single short sentence . et al., 2017; Gehrmann e.t., 2018; Zhong ee., 2019) focus on single-headline generation.
Approach: They propose a method to generate multiple headlines with keyphrases of user interests . they propose generating multiple keyphrase-relevant headlines using a transformer decoder .
Outcome: The proposed method achieves state-of-the-art in terms of quality and diversity.
Extractive Headline Generation Based on Learning to Rank for Community Question Answering (C18-1)

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Challenge: Community question answering (CQA) forums do not always have appropriate headlines because of user-generated content.
Approach: They propose an extractive headline generation method that extracts the most informative substring from each question as its headline.
Outcome: The proposed method outperforms baselines including a prefix-based method . it uses the prefix of a question as its headline to create the most informative substring .
Generating User-Engaging News Headlines (2023.acl-long)

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Challenge: Personalized news recommendation systems present the same headline to all users, making it difficult for them to understand the connection between their interests and the recommended article.
Approach: They propose a framework that incorporates user profiling to generate personalized headlines and a combination of automated and human evaluation methods to determine user preference for personalized headline generation.
Outcome: The proposed framework can generate personalized headlines that meet the needs of a diverse audience.

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