What Makes a Good Dataset for Symbol Description Reading?
This work addresses the mathematical identifier description reading task for document understanding, but it is incremental as it builds on existing noun phrase ranking approaches.
The paper tackles the problem of interpreting mathematical formulas in documents by identifying symbols and extracting their descriptions, introducing a new dataset with 7508 annotated identifier occurrences and reporting experimental results for state-of-the-art and novel methods.
The usage of mathematical formulas as concise representations of a document's key ideas is common practice. Correctly interpreting these formulas, by identifying mathematical symbols and extracting their descriptions, is an important task in document understanding. This paper makes the following contributions to the mathematical identifier description reading (MIDR) task: (i) introduces the Math Formula Question Answering Dataset (MFQuAD) with $7508$ annotated identifier occurrences; (ii) describes novel variations of the noun phrase ranking approach for the MIDR task; (iii) reports experimental results for the SOTA noun phrase ranking approach and our novel variations of the approach, providing problem insights and a performance baseline; (iv) provides a position on the features that make an effective dataset for the MIDR task.