Systematic Exploration of Dialogue Summarization Approaches for Reproducibility, Comparative Assessment, and Methodological Innovations for Advancing Natural Language Processing in Abstractive Summarization

TL;DR

This paper systematically evaluates dialogue summarization models on the AMI dataset, emphasizing reproducibility, comparative assessment, and methodological innovations to advance NLP research.

Contribution

It provides a comprehensive reproducibility study of dialogue summarization models, highlighting discrepancies and proposing methodological improvements for better evaluation.

Findings

Reproducibility issues identified in dialogue summarization models.

Hierarchical Memory Networks and Pointer-Generator Networks evaluated.

Human assessment used to measure summary informativeness and quality.

Abstract

Reproducibility in scientific research, particularly within the realm of natural language processing (NLP), is essential for validating and verifying the robustness of experimental findings. This paper delves into the reproduction and evaluation of dialogue summarization models, focusing specifically on the discrepancies observed between original studies and our reproduction efforts. Dialogue summarization is a critical aspect of NLP, aiming to condense conversational content into concise and informative summaries, thus aiding in efficient information retrieval and decision-making processes. Our research involved a thorough examination of several dialogue summarization models using the AMI (Augmented Multi-party Interaction) dataset. The models assessed include Hierarchical Memory Networks (HMNet) and various versions of Pointer-Generator Networks (PGN), namely PGN(DKE), PGN(DRD), PGN(DTS), and PGN(DALL). The primary objective was to evaluate the informativeness and quality of the summaries generated by these models through human assessment, a method that introduces subjectivity and variability in the evaluation process. The analysis began with Dataset 1, where the sample standard deviation of 0.656 indicated a moderate dispersion of data points around the mean.