VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search

@article{Mishra2021VisualTextRankUG,
  title={VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search},
  author={Shaunak Mishra and Mikhail Kuznetsov and Gaurav Srivastava and Maxim Sviridenko},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.02725}
}
Numerous online stock image libraries offer high quality yet copyright free images for use in marketing campaigns. To assist advertisers in navigating such third party libraries, we study the problem of automatically fetching relevant ad images given the ad text (via a short textual query for images). Motivated by our observations in logged data on ad image search queries (given ad text), we formulate a keyword extraction problem, where a keyword extracted from the ad text (or its augmented… Expand

Figures and Tables from this paper

TSI: An Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity
TLDR
An ad text strength indicator (TSI) which predicts the click-through-rate (CTR) for an input ad text, fetches similar existing ads to create a neighborhood around the input ad, and compares the predicted CTRs in the neighborhood to declare whether theinput ad is strong or weak is proposed. Expand

References

SHOWING 1-10 OF 27 REFERENCES
Biased TextRank: Unsupervised Graph-Based Content Extraction
TLDR
This work presents two applications of Biased TextRank: focused summarization and explanation extraction, and shows that the algorithm leads to improved performance on two different datasets by significant ROUGE-N score margins. Expand
Guiding creative design in online advertising
TLDR
This work introduces a recommender system which provides a list of desirable keywords for a given brand which can serve as underlying themes, and guide the strategist in finalizing the image and text for the brand's ad creative. Expand
Automatic Understanding of Image and Video Advertisements
TLDR
The novel problem of automatic advertisement understanding is proposed, and a dataset of 64,832 image ads and 3,477 video ads is created to enable research on this problem. Expand
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider varietyExpand
ADVISE: Symbolism and External Knowledge for Decoding Advertisements
TLDR
This work forms the ad understanding task as matching the ad image to human-generated statements that describe the action that the ad prompts, and the rationale it provides for taking this action, and proposes a method that outperforms the state of the art on this task. Expand
Image Captioning: Transforming Objects into Words
TLDR
This work introduces the Object Relation Transformer, a approach to image captioning that builds upon this approach by explicitly incorporating information about the spatial relationship between input detected objects through geometric attention. Expand
Understanding Consumer Journey using Attention based Recurrent Neural Networks
TLDR
An attention based recurrent neural network (RNN) which ingests a user activity trail, and predicts the user's conversion probability along with attention weights for each activity (analogous to its position in the funnel) is proposed. Expand
Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of sceneExpand
Distributed Representations of Words and Phrases and their Compositionality
TLDR
This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling. Expand
A Large Scale Prediction Engine for App Install Clicks and Conversions
TLDR
This paper describes how a scalable machine learning pipeline was built from scratch to predict the probability of users clicking and installing apps in response to ad impressions and dives into how sequential model training, deep learning, and transfer learning resulted in a further 7% lift in conversion rate and 11% lifts in revenue. Expand
...
1
2
3
...