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Generating factual, long-form text such as Wikipedia articles raises threekey challenges: how to gather relevant evidence, how to structure informationinto well-formed text, and how to ensure that the generated text is factuallycorrect. We address these by developing a model for English text that uses aretrieval mechanism to identify relevant supporting information on the web anda cache-based pre-trained encoder-decoder to generate long-form biographiessection by section, including citation information. To assess the impact ofavailable web evidence on the output text, we compare the performance of ourapproach when generating biographies about women (for which less information isavailable on the web) vs. biographies generally. To this end, we curate adataset of 1,500 biographies about women. We analyze our generated text tounderstand how differences in available web evidence data affect generation. Weevaluate the factuality, fluency, and quality of the generated texts usingautomatic metrics and human evaluation. We hope that these techniques can beused as a starting point for human writers, to aid in reducing the complexityinherent in the creation of long-form, factual text.

Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies