This study proposes a robust and semantically enriched framework for conversation understanding based on a composite distributed representation that incorporates both temporal adjacency and semantic proximity between utterances, enabling the visualization of key conversational connections.
Abstract
The analysis of spoken data from panel discussions, policy dialogues, and educational meetings has gained increasing importance in both academic research and professional practice. However, conventional approaches to Japanese conversation analysis have relied heavily on keyword matching or surface‑level text similarity, making it difficult to capture deeper semantic relationships, topic transitions, and latent discourse structures. In addition, Japanese natural language processing pipelines often rely on environment-sensitive morphological analyzers, which hinder reproducibility and large-scale processing. To address these limitations, this study proposes a robust and semantically enriched framework for conversation understanding based on a composite distributed representation. The proposed method integrates three layers of linguistic information: (1) contextual sentence embeddings generated by a multilingual transformer model, (2) word embeddings obtained from fastText, and (3) co‑occurrence vectors that capture lexical association patterns within the conversation. Sudachi is employed for Japanese text preprocessing to ensure stable and reproducible morphological analysis. By combining these components into a unified composite vector, the framework simultaneously represents global sentence‑level meaning and local lexical relationships. Using this representation, a directed graph is constructed that incorporates both temporal adjacency and semantic proximity between utterances, enabling the visualization of key conversational connections. To evaluate the effectiveness of the composite representation, dimensionality‑reduction algorithms are applied to examine whether semantically similar utterances naturally form coherent clusters in low‑dimensional space. The resulting clusters are assessed for consistency and interpretability, demonstrating that the proposed representation successfully captures meaningful conversational structure.
A multilevel-attention and hybrid-embedding framework that integrates FastText subword representations with multilingual BERT with potential applicability to other African languages is proposed to improve semantic understanding and context retention in conversational agents for Igbo.
G. C. Uzoaru, I. Ayogu, J. N. Odii et al.· Journal of the Nigerian Soci...· 0 citations
Abstractive text summarization is a critical yet challenging task in natural language processing, requiring models to generate concise, coherent, and factually accurate summaries. Existing transformer-based architectures, such as BART and T5, often underperform in scenarios where language semantics evolve over time or when lexical diversity is limited. These limitations result in summaries that lack contextual relevance and semantic generalization, especially in dynamic domains such as news reporting. In this work, we present a lightweight hybrid abstractive summarization model that enhances temporal awareness and linguistic flexibility. The model integrates synonym-based augmentation and time-sensitive embeddings into a base transformer encoder-decoder architecture, enabling the generation of temporally aligned and semantically enriched summaries. To further improve reliability, we introduce a factual alignment evaluation module that assesses entity-level consistency using named entity recognition. We conduct extensive experiments on two benchmark datasets, XSum and CNN/Daily Mail, and evaluate the model using a wide range of metrics, including ROUGE, BLEU, METEOR, BERTScore, and factual consistency checks. The ablation study confirms the individual contributions of temporal modeling and synonym enrichment, with the combined model achieving up to 8.3% ROUGE-1 and 7.9% METEOR improvements over the baseline. The model demonstrates practical applicability for summarization systems requiring both temporal relevance and semantic robustness, while maintaining CPU efficiency and interpretability. Implications for downstream tasks like news trend analysis are also explored.
K. Abdalgader, Falah Y. H. Ahmed, Blen Wassie et al.· Discover Artificial Intellig...· 0 citations
An integrated framework based on transformer architecture for topic modeling and sentiment analysis for Hindi and Italian social-media discourse, customer reviews and news corpus is introduced and it is suggested that there is clear benefit for morphologically complex text and mixed script text for using contextual embeddings and language-specific pretraining.
Sunita Basalingayya, T. J. Peter· Journal of Intelligent Decis...· 0 citations
Integrating structured knowledge graphs (KGs) with Large Language Models (LLMs) is essential for trustworthy, knowledge intensive conversational systems. However, existing Retrieval Augmented Generation (RAG) methods typically rely on a retrieval-as-context paradigm that linearizes structured subgraphs into unstructured prompt tokens. This approach not only flattens rich structural dependencies but also leads to context inflation and evidence attenuation in multi-turn dialogues. To address these limitations, we propose KGA-LM, a framework that integrates external knowledge via representation-level grounding. Rather than treating retrieved evidence as transient input artifacts, KGA-LM encodes compact multi-hop subgraphs using a Graph Transformer and fuses them into the LLM decoder through a compatibility-aware latent interface. This design aligns the heterogeneous latent spaces of the graph encoder and the LLM, while a dual-gated fusion mechanism dynamically regulates the influence of non-parametric graph evidence across turns. Experiments on multiple conversational benchmarks demonstrate that KGA-LM significantly improves factual accuracy and reduces hallucination compared to prompt-linearized baselines. Crucially, by decoupling knowledge injection from prompt length, our approach mitigates retrieval signal decay under long contexts, offering a superior trade-off between grounding quality and inference efficiency.
Yunfei Li, Chengfei Liu, Rui Zhou et al.· Proceedings of the 32nd ACM...· 0 citations
A novel, layered conceptual framework is introduced that organizes research in CDR across four key dimensions: User Layer, System Layer, Data Layer, and Evaluation Layer and identifies core challenges in CDR, including the lack of standardized evaluation benchmarks and limited support for ambiguous or evolving user intent.
Lisa-Yao Gan, Johanna Walker, E. Simperl et al.· Information Systems Frontier...· 0 citations
The exponential growth of social media has reshaped global communication and decision‐making in business, politics and economics. Yet, the sheer volume and informal, unstructured nature of user‐generated content present major challenges for meaningful analysis. This study introduces a novel abstractive summarisation framework designed to distill coherent and semantically rich summaries from social media discussions. Built on the T5 transformer architecture and enhanced through targeted transfer learning, the system effectively captures the fragmented, slang‐rich language patterns common across platforms. Evaluation is conducted using a suite of semantic‐aware metrics—including ROUGE‐WE, SUPERT and Shannon entropy—alongside human‐centric criteria such as coherence, fluency, consistency and lexical diversity. Results show that the proposed model consistently outperforms mainstream summarisation methods, including advanced systems like ChatGPT, particularly in preserving semantic alignment and improving readability under noisy conditions. Comparative analysis underscores the framework's robustness in handling unstructured, domain‐specific discourse. These findings position the model as a valuable tool for real‐time, high‐volume social media analytics. Future work will explore hybrid neural quality assessment and interactive feedback mechanisms to further enhance domain adaptability and summary fluency.
A. Papagiannopoulou, C. Angeli· Expert systems· 0 citations