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Physical and digital documents often contain visually rich information. With such information, there is no strict order- ing or positioning in the document where the data values must appear. Along with textual cues, these documents often also rely on salient visual features to define distinct semantic boundaries and augment the information they disseminate. When performing information extraction (IE), traditional techniques fall short, as they use a text-only representation and do not consider the visual cues inherent to the layout of these documents. We propose VS2, a generalized approach for information extraction from heterogeneous visually rich documents. There are two major contributions of this work. First, we propose a robust segmentation algorithm that de- composes a visually rich document into a bag of visually iso- lated but semantically coherent areas, called logical blocks. Document type agnostic low-level visual and semantic fea- tures are used in this process. Our second contribution is a distantly supervised search-and-select method for identify- ing the named entities within these documents by utilizing the context boundaries defined by these logical blocks. Ex- perimental results on three heterogeneous datasets suggest that the proposed approach significantly outperforms its text-only counterparts on all datasets. Comparing it against the state-of-the-art methods also reveal that VS2 performs comparably or better on all datasets.
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Improving information extraction from visually rich documents using visual span representations
Along with textual content, visual features play an essential role in the semantics of visually rich documents. Information extraction (IE) tasks perform poorly on these documents if these visual cues are not taken into account. In this paper, we present Artemis - a visually aware, machine-learning-based IE method for heterogeneous visually rich documents. Artemis represents a visual span in a document by jointly encoding its visual and textual context for IE tasks. Our main contribution is two-fold. First, we develop a deep-learning model that identifies the local context boundary of a visual span with minimal human-labeling. Second, we describe a deep neural network that encodes the multimodal context of a visual span into a fixed-length vector by taking its textual and layout-specific features into account. It identifies the visual span(s) containing a named entity by leveraging this learned representation followed by an inference task. We evaluate Artemis on four heterogeneous datasets from different domains over a suite of information extraction tasks. Results show that it outperforms state-of-the-art text-based methods by up to 17 points in F1-score.
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- Award ID(s):
- 1910356
- PAR ID:
- 10268365
- Date Published:
- Journal Name:
- Proceedings of the VLDB Endowment
- Volume:
- 14
- Issue:
- 5
- ISSN:
- 2150-8097
- Page Range / eLocation ID:
- 822 to 834
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.14778/3446095.3446104
