What does this research mean for the field?

The Sentiment-Aware Redundancy-Minimized Maximum Marginal Relevance (SA-RMMR) framework outperforms classical extractive approaches and transformer-based models in generating product review summaries that preserve aspects, sentiments, and informational diversity. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop an extractive summarization framework that effectively captures key product aspects and aligns customer sentiment while eliminating redundant information.

September 14, 2026Discover Artificial IntelligenceOpen Access

Sentiment-guided semantic ranking framework for aspect-aware product review summarization

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Authors

VKVijay H. KalmaniShivaji University AAAmol C. AdamutheInternational Sakharov Environmental Institute PBPooja BaganeSymbiosis International University

Discussion

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Implication

Experimental evaluation demonstrates enhanced aspect coverage and sentiment alignment in e-commerce review summarization, highlighting the efficacy of multi-objective semantic ranking.

Key Points

To develop an extractive summarization framework that effectively captures key product aspects and aligns customer sentiment while eliminating redundant information.
Constructed the Sentiment-Aware Redundancy-Minimized Maximum Marginal Relevance (SA-RMMR) framework combining SBERT representations, TF-IDF and part-of-speech aspect extraction, and VADER-based sentiment scoring.
Introduced semantic centrality estimation, adaptive retrieval confidence, reward mixing, and a Composite Quality Index (CQI) to evaluate semantic relevance, aspect coverage, sentiment alignment, and redundancy.
Benchmarked the framework on multiple Amazon product review datasets against conventional extractive techniques and transformer models including BART, PEGASUS, and FLAN-T5.
SA-RMMR attained the highest performance across evaluated models for Aspect Coverage (0.835) and Sentiment Alignment (0.719).
The framework demonstrated strong semantic relevance with a BERTScore-F1 of 0.825, significantly outperforming BART, PEGASUS, and FLAN-T5 in overall summary quality.
Ablation analyses confirmed that semantic sentence representations, aspect awareness, adaptive retrieval, and redundancy elimination each contributed positively to summary performance.

Cite This Study

Kalmani et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3440926e14a848b2129 https://doi.org/10.1007/s44163-026-02176-1

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Implication

Experimental evaluation demonstrates enhanced aspect coverage and sentiment alignment in e-commerce review summarization, highlighting the efficacy of multi-objective semantic ranking.

Sentiment-guided semantic ranking framework for aspect-aware product review summarization

Discussion

Member takes

Implication

Key Points

Cite This Study

Sentiment-guided semantic ranking framework for aspect-aware product review summarization

Discussion

Member takes

Implication

Key Points

Cite This Study

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