Arie Cattan, Roy Bar-Haim, Yoav Kantor, Lilach Eden
We lifted 9 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| IBM/kpa-hierarchy | canonical | 0 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| edges_to_relations | Not yet run | IBM/kpa-hierarchy/utils.py code served (permissive licence) · get_code("706554340f95780f") |
| filter_topics_by_domains | Not yet run | IBM/kpa-hierarchy/utils.py code served (permissive licence) · get_code("99910774c9a88b9c") |
| get_global_scores | Not yet run | IBM/kpa-hierarchy/eval_pairwise_scores.py code served (permissive licence) · get_code("3bc26a42348bec14") |
| get_leaves | Not yet run | IBM/kpa-hierarchy/TNFTreePredictor.py code served (permissive licence) · get_code("c456ddd5aa98f239") |
| get_max_f1_res | Not yet run | IBM/kpa-hierarchy/eval_pairwise_scores.py code served (permissive licence) · get_code("c459264667d509fa") |
| get_n_kps_from_topic_scores_df | Not yet run | IBM/kpa-hierarchy/KPH.py code served (permissive licence) · get_code("836491c52754e8d9") |
| get_roots | Not yet run | IBM/kpa-hierarchy/TNFTreePredictor.py code served (permissive licence) · get_code("4093ff3dfcc633e2") |
| get_sum_subtree | Not yet run | IBM/kpa-hierarchy/TNFTreePredictor.py code served (permissive licence) · get_code("148e08084c0d5347") |
| load_pairwise_scores | Not yet run | IBM/kpa-hierarchy/eval_pairwise_scores.py code served (permissive licence) · get_code("9be44491001d0ed7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Key Point Analysis (KPA) has been recently proposed for deriving fine-grained insights from collections of textual comments. KPA extracts the main points in the data as a list of concise sentences or phrases, termed key points, and quantifies their prevalence. While key points are more expressive than word clouds and key phrases, making sense of a long, flat list of key points, which often express related ideas in varying levels of granularity, may still be challenging. To address this limitation of KPA, we introduce the task of organizing a given set of key points into a hierarchy, according to their specificity. Such hierarchies may be viewed as a novel type of Textual Entailment Graph. We develop THINKP, a high quality benchmark dataset of key point hierarchies for business and product reviews, obtained by consolidating multiple annotations. We compare different methods for predicting pairwise relations between key points, and for inferring a hierarchy from these pairwise predictions. In particular, for the task of computing pairwise key point relations, we achieve significant gains over existing strong baselines by applying directional distributional similarity methods to a novel distributional representation of key points, and further boost performance via weak supervision. https://github.com/IBM/kpa-hierarchy Many organizations face the challenge of extracting insights from large collections of textual comments, such as user reviews, survey responses, and feedback from customers or employees. Current text analytics tools summarize such datasets via word clouds (Heimerl et al., 2014) or key phrases (Hasan and Ng, 2014;Merrouni et al., 2019), which are often too crude to capture fine-grained insights.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.03853")
get_code_for_paper("2306.03853")
have("2306.03853")
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