Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion (2022)

Abstract: We advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs. Starting from well-founded assumptions about word graphs, we present simple but reliable path-reranking and topic segmentation schemes. Robustness of our method is demonstrated on datasets across multiple domains, including meetings, interviews, movie scripts, and day-to-day conversations. We also identify possible avenues to augment our heuristic-based system with deep learning. We open-source our code, to provide a strong, reproducible baseline for future research into unsupervised dialogue summarization.

## introduction compared to traditional text summarization, dialogue summarization introduces a unique challenge: conversion of first-and second-person speech into third-person reported speech. such discrepancy between the observed text and expected model output puts greater emphasis on abstractive transduction than in traditional summarization tasks. the disorientation is further exacerbated by each of many diverse dialogue types calling for a differing form of transduction -short dialogues require terse abstractions, while meeting transcripts require summaries by agenda. thus, despite the steady emergence of dialogue summarization datasets, the field of dialogue summarization is still bottlenecked by a scarcity of training data. to train a truly robust dialogue summarization model, one requires transcript-summary pairs not only across diverse dialogue domains, but also across multiple dialogue types as well. the lack of diverse annotated summarization data is especially pronounced in low-resourced languages. from such state of the literature, we identify a need for unsupervised dialogue summarization. our method builds upon previous research on unsupervised summarization using word graphs. starting from the simple assumption that a good summary sentence is at least as informative as any single input sentence, we develop novel schemes for path extraction from word graphs. our contributions are as follows: with just an off-the-shelf part-of-speech (pos) tagger and a list of stopwords, our model can be applied across different types of dialogue summarization. ## summarization strategy in following subsections we outline our proposed summarization process.