DeepGen: Diverse Search Ad Generation and Real-Time Customization (2022.emnlp-demos)
Copied to clipboard
| 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. |
Similar Papers
An Empirical Study of Generating Texts for Search Engine Advertising (2021.naacl-industry)
Copied to clipboard
| 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. |
OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising (2025.coling-industry)
Copied to clipboard
| 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. |
Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation (2024.acl-long)
Copied to clipboard
| 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. |
ATGen: A Framework for Active Text Generation (2025.acl-demo)
Copied to clipboard
Akim Tsvigun, Daniil Vasilev, Ivan Tsvigun, Ivan Lysenko, Talgat Bektleuov, Aleksandr Medvedev, Uliana Vinogradova, Nikita Severin, Mikhail Mozikov, Andrey Savchenko, Ilya Makarov, Grigorev Rostislav, Ramil Kuleev, Fedor Zhdanov, Artem Shelmanov
| Challenge: | Despite the surging popularity of natural language generation tasks, the application of active learning (AL) to NLG has been limited. |
| Approach: | They propose a framework that bridges AL with text generation tasks and provides a unified platform for smooth implementation and benchmarking of novel AL strategies tailored to NLG tasks. |
| Outcome: | The proposed framework simplifies AL-empowered annotation in NLG tasks using both human annotators and automatic annotation agents based on large language models (LLMs). |
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation (D18-1)
Copied to clipboard
| Challenge: | Existing text generation methods tend to produce repeated and ”boring” expressions. |
| Approach: | They propose a model that assigns low reward for repeatedly generated text and high reward for ”novel” and fluent text, and a novel language-model based discriminator which can distinguish novel text from repeated text without the saturation problem. |
| Outcome: | The proposed model generates more diverse and informative text than existing baselines on review generation and dialogue generation tasks. |
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning (2022.naacl-industry)
Copied to clipboard
| 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. |
Topic-driven Ensemble for Online Advertising Generation (2020.coling-main)
Copied to clipboard
| 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. |
Controlled Language Generation for Language Learning Items (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models have resulted in success in generating fluent English text. |
| Approach: | They propose to employ natural language generation to rapidly generate English language items . they experiment with deep pretrained models and develop methods for controlling items for factors relevant in language learning . |
| Outcome: | The proposed framework shows high grammatically scores for all models and higher complexity over baseline models. |
CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese (2024.lrec-main)
Copied to clipboard
| Challenge: | Despite numerous efforts in ad text generation, the aspect of diversifying a text has received limited attention, particularly in non-English languages like Japanese. |
| Approach: | They present a dataset for ad text generation in Japanese using annotators to examine the capabilities of recent NLG models. |
| Outcome: | The proposed dataset includes 3,980 ad texts written by experts taking into account various aspects of ade appeals. |
Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction. |
| Approach: | They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time. |
| Outcome: | The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions. |