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This story was constructed automatically by using an updated version of AlNoamany's Algorithm. For more information about how this algorithm was developed, how it works, and its evaluation, please consult:
- Y. AlNoamany, M. C. Weigle, and M. L. Nelson, "Generating Stories From Archived Collections," in Proceedings of the 2017 ACM on Web Science Conference, pp. 309–318, ACM Press, 2017. https://doi.org/10.1145/3091478.3091508.
- Y. AlNoamany, M. C. Weigle, and M. L. Nelson, " Stories From the Past Web," Tech. Rep. 1705.06218, May, 2017. https://arxiv.org/abs/1705.06218.
We employed the DSA toolkit to tell this story with the following steps.
- Hypercane selected the resources and generated the metadata for the story:
- It first executed AlNoamany's Algorithm:
- Discovered the Memento TimeMaps for the collection
- Analyzed these TimeMaps and reported the memento URI-Ms that were on-topic
- Filtered the content of those URI-Ms to find non-duplicates
- Filtered the content of those URI-Ms to find English language documents
- Sliced the collection by memento-datetime
- Clustered the mementos in each slice by Simhash distance
- Ranked the mementos in each cluster by a ranking equation
- Filtered each cluster for the highest ranking mementos
- Ordered each memento by publication date
- It used Archive-It Utilities to extract all metadata from the collection, such as who created the collection and the collection's name
- It analyzed all mementos to automatically discover the most frequent sumgrams and named entities present in the overall story
- It analyzed all images in these mementos to automatically select the best image for the overall story
- It then formatted the data for the story based on all of this input
- Raintale took the input from Hypercane and rendered the final product with information supplied by MementoEmbed