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Schema-Driven Actionable Insight Generation and Smart Recommendation

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Allmin Susaiyah, Aki Härmä, Milan Petković

Computation and Language · Submitted 24 Jul 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show preliminary qualitative results of the insights generated using our technique and demonstrate its ability to adapt to feedback.

how it is stated · asserted 0.99 read the record →

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arXiv ID2307.13176 (version 1)
Pith IDYWKSSDR2, Pith's permanent record of this paper and its review
CitationAki H\"arm\"a, Allmin Susaiyah, Milan Petkovi\'c. "Schema-Driven Actionable Insight Generation and Smart Recommendation." arXiv:2307.13176 (2023). https://pith.science/paper/YWKSSDR2
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,773 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.66 · human participants 0.40 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.10 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In natural language generation (NLG), insight mining is seen as a data-to-text task, where data is mined for interesting patterns and verbalised into 'insight' statements. An 'over-generate and rank' paradigm is intuitively used to generate such insights. The multidimensionality and subjectivity of this process make it challenging. This paper introduces a schema-driven method to generate actionable insights from data to drive growth and change. It also introduces a technique to rank the insights to align with user interests based on their feedback. We show preliminary qualitative results of the insights generated using our technique and demonstrate its ability to adapt to feedback.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In natural language generation (NLG), insight mining is seen as a data-to-text task, where data is mined for interesting patterns and verbalised into 'insight' statements. background 1.00 0.05 0.03 asserted 0.99
s1 An 'over-generate and rank' paradigm is intuitively used to generate such insights. background 1.00 0.06 0.03 asserted 0.92
s2 The multidimensionality and subjectivity of this process make it challenging. background 0.80 0.06 0.02 asserted 1.00
s3 This paper introduces a schema-driven method to generate actionable insights from data to drive growth and change. contribution 0.95 0.89 0.30 asserted 1.00
s4 It also introduces a technique to rank the insights to align with user interests based on their feedback. contribution 0.86 0.87 0.11 asserted 1.00
s5 We show preliminary qualitative results of the insights generated using our technique and demonstrate its ability to adapt to feedback. result 0.97 0.96 0.38 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.91.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In natural language generation (NLG), insight mining is seen as a data-to-text task, where data is mined for interesting patterns and verbalised into 'insight' statements. An 'over-generate and rank' paradigm is intuitively used to generate such insights. The multidimensionality and subjectivity of this process make it challenging. This paper introduces a schema-driven method to generate actionable insights from data to drive growth and change. It also introduces a technique to rank the insights to align with user interests based on their feedback. We show preliminary qualitative results of the insights generated using our technique and demonstrate its ability to adapt to feedback.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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