We present approaches to estimate content-aware bibliometrics to quantitatively measure the scholarly impact of a publication. Traditional measures to assess quality-related aspects such as citation counts and h-index, do not take into account the content of the publications, which limits their ability to provide rigorous quality-related metrics and can significantly skew the results. Our proposed metric, denoted by Content Informed Index (CII), uses the content of the paper as a source of distant-supervision, to weight the edges of a citation network. These content-aware weights quantify the information in the citation i.e., these weights quantify the extent to which the cited-node informs the citing-node. The weights convert the original unweighted citation network to a weighted one. Consequently, this weighted network can be used to derive impact metrics for the various entities involved, like the publications, authors etc. We evaluate the weights estimated by our approach on three manually annotated datasets, where the annotations quantify the extent of information in the citation. Particularly, we evaluate how well the ranking imposed by our approach associates with the ranking imposed by the manual annotations. The proposed approach achieves up to 103% improvement in performance as compared to second best performing approach.
|Original language||English (US)|
|Journal||CEUR Workshop Proceedings|
|State||Published - 2021|
|Event||2021 Workshop on Scientific Document Understanding, SDU 2021 - Virtual, Online|
Duration: Feb 9 2021 → …
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Copyright © 2021for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).