| Challenge: | Existing methods for automating online advertising use open data . subdomains of text vary in use and can lead to reduced quality of adverts generation. |
| Approach: | They propose a neural network-based approach for the automatic generation of online advertising using texts from given webpages as sources. |
| Outcome: | The proposed approach significantly improves the quality of online advertising generated on a Russian dataset. |
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An Empirical Study of Generating Texts for Search Engine Advertising (2021.naacl-industry)
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| Challenge: | Existing studies on neural language generation have not evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. |
| Approach: | They propose to integrate a reinforcement learning framework into an end-to-end sequence-tosequence (Seq2S) model and demonstrate how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads. |
| Outcome: | The proposed method improves the ads’ impact, deploys the models to a product, and evaluates the generated ads. |
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning (2022.naacl-industry)
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| Challenge: | a paper focuses on automatically generating the text of an ad to capture user interest for achieving higher click-through rate. |
| Approach: | They propose a CTR-driven advertising text generation approach to generate ad texts based on user reviews. |
| Outcome: | The proposed approach outperforms existing approaches on industrial datasets and on large-scale unpaired reviews. |
PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning (2022.emnlp-industry)
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Zeyang Lei, Chao Zhang, Xinchao Xu, Wenquan Wu, Zheng-yu Niu, Hua Wu, Haifeng Wang, Yi Yang, Shuanglong Li
| Challenge: | Online advertisement text generation models have achieved remarkable success in generating high-quality text ads, but some challenges remain, such as low-resource scenarios and training efficiency for multiple ad tasks. |
| Approach: | They propose a unified text ad generation framework with multi-task prompt learning to tackle low-resource ade generation problem and a multi-step prompt learning mechanism to efficiently solve multiple aed generation tasks. |
| Outcome: | The proposed framework outperforms the state-of-the-art on offline and online metrics. |
Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation (2024.acl-long)
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| Challenge: | Existing benchmarks and problem sets for automatic ad text generation are lacking . however, the growing volume of search queries has fueled research on the automatic generation of ads. |
| Approach: | They propose to standardize the task of automatic ad text generation (ATG) using a benchmark dataset, CAMERA, to enable the utilization of multi-modal information and facilitate industry-wise evaluations. |
| Outcome: | The proposed dataset standardizes the task of automatic ad text generation (ATG) it shows that existing metrics align with human evaluations and that the proposed methods can be used to improve the quality of the results. |
DeepGen: Diverse Search Ad Generation and Real-Time Customization (2022.emnlp-demos)
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| Challenge: | Existing systems that generate ads manually are not effective in generating ad copy and generating millions of ads for large businesses. |
| Approach: | They propose a system that generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed. |
| Outcome: | The proposed system generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed. |
OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising (2025.coling-industry)
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| Challenge: | Conventionally, keyword decision-making in sponsored search advertising relies on deep generation-based methods. |
| Approach: | They propose an LLM agent-based method that dynamically monitors KPI changes and adapts keyword generation in real-time. |
| Outcome: | The proposed method shows significant improvements across various metrics and emphasizes the importance of each component. |
OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent (2025.emnlp-main)
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| Challenge: | Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. |
| Approach: | They propose a keyword generation framework that is On-the-fly and Multi-objective to automate keyword generation. |
| Outcome: | Experiments show that OMS outperforms existing methods in keyword generation . relying on large-scale query-keyword data is a major limitation, authors say . |
Neural Storyline Extraction Model for Storyline Generation from News Articles (N18-1)
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| Challenge: | Existing approaches to storyline generation are domain dependent and cannot deal with unseen event types. |
| Approach: | They propose a neural network-based approach to extract structured representations and evolution patterns of storylines without using annotated data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on accuracy and efficiency on three news corpora and it is based on supervised models. |
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation (2021.acl-long)
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| Challenge: | Existing neural generation models fall short of coherence, thus requiring efficient content planning. |
| Approach: | They propose a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. |
| Outcome: | The proposed model outperforms competing models on argument generation and writing articles using New York Times’ Opinion section. |
Analyzing Online Political Advertisements (2021.findings-acl)
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| Challenge: | Online political advertising is an integral part of modern digital election campaigning. |
| Approach: | They propose to use textual and visual information from pre-trained neural models to infer the political ideology of an ad sponsor and identify whether the sponsor is an official political party or a third-party organization. |
| Outcome: | The proposed approach outperforms state-of-the-art methods for generic commercial ad classification and linguistic analysis to study the characteristics of political ads discourse. |