Business documents come in a variety of structures, formats and information needs which makes information extraction a challenging task. Due to these variations, having a document generic model which can work well across all types of documents for all the use cases seems far-fetched. For document-specific models, we would need customized document-specific labels. We introduce DoSA (Document Specific Automated Annotations), which helps annotators in generating initial annotations automatically using our novel bootstrap approach by leveraging document generic datasets and models. These initial annotations can further be reviewed by a human for correctness. An initial document-specific model can be trained and its inference can be used as feedback for generating more automated annotations. These automated annotations can be reviewed by humanin-the-loop for the correctness and a new improved model can be trained using the current model as pre-trained model before going for the next iteration. In this paper, our scope is limited to Form like documents due to limited availability of generic annotated datasets, but this idea can be extended to a variety of other documents as more datasets are built. An opensource ready-to-use implementation is made available on GitHub.

This paper was submitted for Financial Narrative Summarization (FNS) task in FNP-2022 workshop. The objective of the task was to generate not more than 1000 words summaries for the annual financial reports written in English, Spanish and Greek languages. The central idea of this paper is to demonstrate automatic ways of identifying key narrative sections and their contributions towards generating summaries of financial reports. We have observed a few limitations in the previous works: First, the complete report was being considered for summary generation instead of key narrative sections. Second, many of the works followed manual or heuristic-based techniques to identify narrative sections. Third, sentences from key narrative sections were abruptly dropped to limit the summary to the desired length. To overcome these shortcomings, we introduced a novel approach to automatically learn key narrative sections and their weighted contributions to the reports. Since the summaries may come from various parts of the reports, the summary generation process was distributed amongst the key narrative sections based on the weights identified, later combined to have an overall summary. We also showcased that our approach is adaptive to various report formats and languages.