Abstract
Scientific charts in the web, whether as images or embedded in digital documents, contain valuable information that is not fully available to information retrieval tools. The information used to describe these charts is typically extracted from the image metadata rather than the information the graphic was initially designed to express. The problem of understanding digital charts found in scholarly documents, and inferring useful textual information from their graphical components is the focus of this study. We present an approach to automatically read the chart data, specifically bar charts, and provide the user with a textual summary of the chart. The proposed method follows a knowledge discovery approach that relies on a versatile graph representation of the chart. This representation is derived from analyzing a chart's original data values, from which useful features are extracted. The data features are in turn used to construct a semantic-graph. To generate a summary, the semantic-graph of the chart is mapped to appropriately crafted protoforms, which are constructs based on fuzzy logic. We verify the effectiveness of our framework by conducting experiments on bar charts extracted from over 1,000 PDF documents. Our preliminary results show that, under certain assumptions, 83% of the produced summaries provide plausible descriptions of the bar charts.
| Original language | English (US) |
|---|---|
| Title of host publication | WS-16-01 |
| Subtitle of host publication | Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence |
| Publisher | AI Access Foundation |
| Pages | 658-663 |
| Number of pages | 6 |
| Volume | WS-16-01 - WS-16-15 |
| ISBN (Electronic) | 9781577357599 |
| State | Published - Jan 1 2016 |
| Event | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: Feb 12 2016 → Feb 17 2016 |
Other
| Other | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
|---|---|
| Country | United States |
| City | Phoenix |
| Period | 2/12/16 → 2/17/16 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
}
Automatic summary generation for scientific data charts. / Al-Zaidy, Rabah A.; Choudhury, Sagnik Ray; Giles, Clyde Lee.
WS-16-01: Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence. Vol. WS-16-01 - WS-16-15 AI Access Foundation, 2016. p. 658-663.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Automatic summary generation for scientific data charts
AU - Al-Zaidy, Rabah A.
AU - Choudhury, Sagnik Ray
AU - Giles, Clyde Lee
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Scientific charts in the web, whether as images or embedded in digital documents, contain valuable information that is not fully available to information retrieval tools. The information used to describe these charts is typically extracted from the image metadata rather than the information the graphic was initially designed to express. The problem of understanding digital charts found in scholarly documents, and inferring useful textual information from their graphical components is the focus of this study. We present an approach to automatically read the chart data, specifically bar charts, and provide the user with a textual summary of the chart. The proposed method follows a knowledge discovery approach that relies on a versatile graph representation of the chart. This representation is derived from analyzing a chart's original data values, from which useful features are extracted. The data features are in turn used to construct a semantic-graph. To generate a summary, the semantic-graph of the chart is mapped to appropriately crafted protoforms, which are constructs based on fuzzy logic. We verify the effectiveness of our framework by conducting experiments on bar charts extracted from over 1,000 PDF documents. Our preliminary results show that, under certain assumptions, 83% of the produced summaries provide plausible descriptions of the bar charts.
AB - Scientific charts in the web, whether as images or embedded in digital documents, contain valuable information that is not fully available to information retrieval tools. The information used to describe these charts is typically extracted from the image metadata rather than the information the graphic was initially designed to express. The problem of understanding digital charts found in scholarly documents, and inferring useful textual information from their graphical components is the focus of this study. We present an approach to automatically read the chart data, specifically bar charts, and provide the user with a textual summary of the chart. The proposed method follows a knowledge discovery approach that relies on a versatile graph representation of the chart. This representation is derived from analyzing a chart's original data values, from which useful features are extracted. The data features are in turn used to construct a semantic-graph. To generate a summary, the semantic-graph of the chart is mapped to appropriately crafted protoforms, which are constructs based on fuzzy logic. We verify the effectiveness of our framework by conducting experiments on bar charts extracted from over 1,000 PDF documents. Our preliminary results show that, under certain assumptions, 83% of the produced summaries provide plausible descriptions of the bar charts.
UR - http://www.scopus.com/inward/record.url?scp=84974575964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84974575964&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84974575964
VL - WS-16-01 - WS-16-15
SP - 658
EP - 663
BT - WS-16-01
PB - AI Access Foundation
ER -