Systems, methods and media are provided for training a snippet extractor to create snippets based on information extracted from published descriptions. In one example, a computer-implemented method includes creating, based on a non-RNN (Recurrent Neural Network) extraction technique performed on the published descriptions, a plurality of base models, each base model including one or more sample description summaries; evaluating the base models using an evaluation technique; selecting an optimum base model; developing a classification model using RNN extraction, the classification model based on description summaries contained in the optimum base model; and using the classification model to train the snippet extractor by machine learning.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for training a snippet extractor to create snippets based on information extracted from published descriptions, the method comprising, by one or more processors: creating, based on a non-RNN (Recurrent Neural Network) extraction technique performed on the published descriptions, a plurality of base models, each base model including one or more sample description summaries; evaluating the base models using an evaluation technique, and selecting an optimum base model; developing a classification model using RNN extraction, the classification model based on description summaries contained in the optimum base model; and using the classification model to train the snippet extractor by machine learning. 2. The method of claim 1, wherein the non-RNN extraction technique is based on or includes one or more of: a graph-based technique, a Latent Semantic Analysis (LSA) technique, a Latent Dirichlet Allocation (LDA) technique, a Naïve Bayes (NB) classification technique, a word-embedding similarity, a Neural Network (NN), a Topic Signature (TS) based technique, and a de-duplication technique. 3. The method of claim 1, wherein the evaluation technique is based on or includes one or more of: a rogue and precision recall, a Latent Semantic Analysis (LSA) and topic overlap, a Kulbach-Leibler (KL) or JS divergence, a largest common substring average, and a summary probability. 4. The method of claim 4, wherein the LSA and topic overlap are based on a cosine or vector-based cosine similarity. 5. The method of claim 4, further comprising assessing the performance of a trained snippet extractor based on an escore metric, the escore metric as defined herein by: Escore = #   search   queries   present   in   the   snippet   leading   to   engagement #   search   queries   leading   to   engagement 6. A system for training a snippet extractor to create snippets based on information extracted from published descriptions, the system comprising: processors; and a memory storing instructions that, when executed by at least one processor among the processors, cause the system to perform operations comprising, at least: creating, based on a non-RNN (Recurrent Neural Network) extraction technique performed on the published descriptions, a plurality of base models, each base model including one or more sample description summaries; evaluating the base models using an evaluation technique, and selecting an optimum base model; developing a classification model using RNN extraction, the classification model based on description summaries contained in the optimum base model; and using the classification model to train the snippet extractor by machine learning. 7. The system of claim 6, wherein the non-RNN extraction technique is based on or includes one or more of: a graph-based technique, a Latent Semantic Analysis (LSA) technique, a Latent Dirichlet Allocation (LDA) technique, a Naïve Bayes (NB) classification technique, a word-embedding similarity, a Neural Network (NN), a Topic Signature (TS) based technique, and a de-duplication technique. 8. The system of claim 6, wherein the evaluation technique is based on or includes one or more of: a rogue and precision recall, a Latent Semantic Analysis (LSA) and topic overlap, a Kulbach-Leibler (KL) or JS divergence, a largest common substring average, and a summary probability. 9. The system of claim 6, wherein the LSA and topic overlap are based on a cosine or vector-based cosine similarity. 10. The system of claim 6, further comprising assessing the performance of a trained snippet extractor based on an escore metric, the escore metric as defined herein by: Escore = #   search   queries   present   in   the   snippet   leading   to   engagement #   search   queries   leading   to   engagement 11. A non-transitory machine-readable medium including instructions that, when read by a machine, cause the machine to perform operations comprising, at least: creating, based on a non-RNN (Recurrent Neural Network) extraction technique performed on the published descriptions, a plurality of base models, each base model including one or more sample description summaries; evaluating the base models using an evaluation technique, and selecting an optimum base model; developing a classification model using RNN extraction, the classification model based on description summaries contained in the optimum base model; and using the classification model to train a snippet extractor by machine learning. 12. The medium of claim 11, wherein the non-RNN extraction technique is based on or includes one or more of: a graph-based technique, a Latent Semantic Analysis (LSA) technique, a Latent Dirichlet Allocation (LDA) technique, a Naïve Bayes (NB) classification technique, a word-embedding similarity, a Neural Network (NN), a Topic Signature (TS) based technique, and a de-duplication technique. 13. The medium of claim 11, wherein the evaluation technique is based on or includes one or more of: a rogue and precision recall, a Latent Semantic Analysis (LSA) and topic overlap, a Kulbach-Leibler (KL) or JS divergence, a largest common substring average, and a summary probability. 14. The medium of claim 11, wherein the LSA and topic overlap are based on a cosine or vector-based cosine similarity. 15. The medium of claim 11, further comprising assessing the performance of a trained snippet extractor based on an escore metric, the escore metric as defined herein by: Escore = #   search   queries   present   in   the   snippet   leading   to   engagement #   search   queries   leading   to   engagement