Quality Content at Scale Through Automated Text Summarization of UGC
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The document discusses using automated text summarization techniques to generate quality content at scale from user-generated content like online product reviews. It proposes a technical plan to download Amazon reviews, remove duplicate sentences using neural semantic textual similarity, and then generate frequently asked questions and corresponding FAQ schema by feeding the review text into a neural question generation model. The goal is to leverage user content and machine learning to automatically create helpful content for websites.
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Quality Content at Scale Through Automated Text Summarization of UGC
- 1. Quality Content at Scale Through Automated Text Summarization of UGC Hamlet Batista
- 2. You can't connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. ― Steve Jobs @hamletbatista
- 3. The SEO Impact of Meta Descriptions 1. Meta descriptions don’t impact rankings and are the last element on a SEO implementation list 2. They are often ignored by Google (over 60% of the time) 3. Yet, this case study showed they can make a measurable difference MoreBeer Case Study, July 28, 2017 https://bit.ly/3lyqOpb @hamletbatista
- 4. Learnings from Paid Search 1. Scientific SEO A/B tests proved the impact of meta descriptions 2. Bootstrapping the experiments with paid search data proved very successful 3. We have over 90% success rate with this technique Martech Advisor Article July 28, 2017 https://bit.ly/2SL7DfI @hamletbatista
- 5. The Two Salesmen 1. Contrasting paid ads vs organic snippets 2. Paid ads focus on compelling benefits while organic snippets focus on copying text from the page 3. This implies search visitors stop and read the search results before clicking PEC Article, January 4, 2018 https://bit.ly/3jOGRii @hamletbatista
- 6. The Two Salesmen 1. Contrasting paid ads vs organic snippets 2. Paid ads focus on compelling benefits while organic snippets focus on copying text from the page 3. This implies search visitors stop and read the search results before clicking PEC Article, January 4, 2018 https://bit.ly/3jOGRii @hamletbatista
- 7. What if the Ads Copy is Boring? 1. ColeParmer didn’thave compelling paid ads to pull ideas from 2. Our experiments with other clients taught us we needed Cialdini’s pricinples in the copy 3. ColeParmer had plenty of product reviews (Social Proof) ColeParmer Case Study, February 19, 2019 https://bit.ly/3dgCMAP @hamletbatista
- 8. Why Use Copy from Reviews? @hamletbatista
- 9. 1. Reciprocity 2. Commitment and consistency 3. Social proof 4. Authority 5. Liking 6. Scarcity ― Robert Cialdini’s Influence Principles https://amzn.to/31az2ML @hamletbatista
- 10. Let’s Generate FAQs from Reviews Copy Here is our Technical Plan: 1. Download Amazon reviews 2. Remove duplicate sentences 3. Generate FAQs (including FAQPage schema) @hamletbatista
- 11. Downloading Amazon Reviews Amazon provides 130M+ customer reviews for research purposes. 1. The files are in TSV format 2. We need to skip error rows (incorrect tab delimiter) 3. Then, filter the reviews we want to use (for example purchase-verified, 5-star reviews with a minimum number of votes) @hamletbatista
- 13. Removing Duplicate Sentences @hamletbatista Google AI Blog: Advances in Semantic Textual Similarity https://bit.ly/2H925cK
- 14. Removing Duplicate Sentences Many reviews have repetitive information. For example: “the product is great/awesome/etc.” These don’t provide any new/useful insights. We can use Neural Semantic Textual Similarity to identify and remove duplicates, even when the text is not syntactically the same. @hamletbatista
- 15. Generating FAQs (Including Schema) Once we have useful review text, we can use it to feed a Neural Question Generation model and produce FAQs We can also generate the corresponding schema. @hamletbatista
- 16. Generating FAQs (Including Schema) Once we have useful review text, we can use it to feed a Neural Question Generation model and produce FAQs. We can also generate the corresponding schema. @hamletbatista
- 17. Resources to Learn More 1. How to Optimize Your Content for Search Questions Using Deep Learning https://blogs.bing.com/webmaster/july-2020/How-to-Optimize- Your-Content-for-Search-Questions-using-Deep-Learning 2. How to Generate Quality FAQs and FAQPage Schemas Automatically with Python https://www.searchenginejournal.com/generate-quality- faqs-faqpage-schemas-with-python/380004/ 3. Example Content Brief, MarketMuse First Draft, Final Draft on the topic of Order Management [pdf] https://blog.marketmuse.com/wp- content/uploads/2020/10/Example-Content-Brief-First-Draft-Final-Draft-Order-Management.pdf For early access 👉 https://bit.ly/2GgCD4j 4. OpenAI’s gigantic GPT-3 hints at the limits of language models for AI https://www.zdnet.com/article/openais-gigantic-gpt-3-hints-at-the- limits-of-language-models-for-ai/ 5. An Introduction to Python for SEO Pros Using Spreadsheets https://www.searchenginejournal.com/introduction-to-python-seo- spreadsheets/342779/
- 18. About RankSense @RankSense www.ranksense.com 33 Wood Ave South Suite 600 Iselin, NJ 08830 USA +1 866.882.2983