Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning (D18-1)
Copied to clipboard
| Challenge: | Pre-trained word embeddings provide significant improvements over untrained embeddables . Feature analysis reveals structural patterns of headline popularity . |
| Approach: | They use a multi-task GRU network to model headline popularity . they find that pre-trained word embeddings provide significant improvements over untrained embeddables . |
| Outcome: | The proposed model improves on pre-trained word embeddings and untrained embeddables . it also improves with the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging . |
Similar Papers
News Headline Grouping as a Challenging NLU Task (2021.naacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed model outperforms human models on a task consisting of 20,056 pairs of headlines on HLGD and a dataset with a binary judgement. |
A Case Study on Neural Headline Generation for Editing Support (N19-2)
Copied to clipboard
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. |
Generating User-Engaging News Headlines (2023.acl-long)
Copied to clipboard
| 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. |
Headline Token-based Discriminative Learning for Subheading Generation in News Article (2023.findings-eacl)
Copied to clipboard
| 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. |
Neural News Recommendation with Heterogeneous User Behavior (D19-1)
Copied to clipboard
| Challenge: | Existing news recommendation methods rely on news click history to model user interest, but data sparsity is a problem . other kinds of user behaviors such as webpage browsing and search queries can provide useful clues of users’ news reading interest. |
| Approach: | They propose to exploit heterogeneous user behaviors to learn news representations from their titles via CNN networks and apply attention networks to select important words. |
| Outcome: | The proposed approach exploits heterogeneous user behaviors on a real-world dataset. |
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2020.acl-main)
Copied to clipboard
| 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 . |
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception (2020.lrec-1)
Copied to clipboard
| Challenge: | Fewer studies address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. |
| Approach: | They propose to annotate 5000 English news headlines with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, and the reader’s perception of the emotion of the headline. |
| Outcome: | The proposed method enables further research on emotion classification, emotion intensity prediction, emotion cause detection and supports qualitative studies. |
Come hither or go away? Recognising pre-electoral coalition signals in the news (2021.emnlp-main)
Copied to clipboard
Ines Rehbein, Simone Paolo Ponzetto, Anna Adendorf, Oke Bahnsen, Lukas Stoetzer, Heiner Stuckenschmidt
| Challenge: | In this paper, we decompose the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a coalition into two related, but distinct tasks. |
| Approach: | They propose a task of recognizing from news coverage the (un)willingness of political parties to form a coalition from text and a sub-task of predicting the polarity of the signal. |
| Outcome: | The proposed approach improves over a strong monolingual transfer learning baseline. |
Weakly Supervised Headline Dependency Parsing (2022.findings-emnlp)
Copied to clipboard
| 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. |
Shironaam: Bengali News Headline Generation using Auxiliary Information (2023.eacl-main)
Copied to clipboard
| 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. |