Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents (2110.10150v2)

Abstract: Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained LLMs (LM) are unable to efficiently process long text for many summarization tasks. In this paper, we propose Summ$^N$, a simple, flexible, and effective multi-stage framework for input texts that are longer than the maximum context length of typical pretrained LMs. Summ$^N$ first splits the data samples and generates a coarse summary in multiple stages and then produces the final fine-grained summary based on it. Our framework can process input text of arbitrary length by adjusting the number of stages while keeping the LM input size fixed. Moreover, it can deal with both single-source documents and dialogues, and it can be used on top of different backbone abstractive summarization models. To the best of our knowledge, Summ$^N$ is the first multi-stage split-then-summarize framework for long input summarization. Our experiments demonstrate that Summ$^N$ outperforms previous state-of-the-art methods by improving ROUGE scores on three long meeting summarization datasets AMI, ICSI, and QMSum, two long TV series datasets from SummScreen, and a long document summarization dataset GovReport. Our data and code are available at https://github.com/psunlpgroup/Summ-N.

Summ$^N$: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents

The paper "Summ$^N$: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents" presents a novel approach to overcoming the challenges posed by the summarization of extended texts, which typically exceed the context length limits of current pretrained LMs. The authors introduce Summ$^N$, a multi-stage framework designed to split lengthy texts into manageable segments, allowing for the generation of comprehensive summaries without truncating context-relevant information.

Framework Overview

Summ$^N$ leverages a multi-stage process, differentiating it as a pioneering method in the domain of long text summarization. The initial stages focus on dividing source texts into smaller, digestible segments before producing intermediary coarse summaries. This segmentation is crucial, as it maintains context dependency and allows all parts of the source text to contribute to the summary generation process. A greedy ROUGE-based algorithm aids in pairing these segments with appropriate target summaries, optimizing information retention.

In subsequent stages, Summ$^N$ employs pre-trained abstractive summarization models to refine these coarse summaries into fine-grained versions. This approach effectively extends the receptive field of summarization models, allowing them to incorporate full context despite the original text's length. Notably, Summ$^N$ can be adapted for both single-source documents and dialogues, showcasing its versatility across different text types.

Experimental Results

Experiments indicate that Summ$^N$ yields superior ROUGE scores compared to existing methods across a diverse set of datasets, including AMI, ICSI, QMSum, SummScreen, and GovReport. The consistent improvement in summarization quality across these varied datasets underscores Summ$^N$'s robustness and effectiveness. Additionally, Summ$^N$ demonstrates significant enhancements over backbone models such as BART, T5, and PEGASUS, confirming the framework's capability to amplify pretrained models’ summarization performance on long input tasks.

Implications and Future Work

This paper provides significant insights into handling long document summarization, proposing mechanisms that efficiently utilize existing Transformer-based models. The ability to adapt various backbone models into the Summ$^N$ framework suggests extensive applications across industries requiring detailed document synthesis, such as legal and technical fields.

Future research could explore optimizing the choice of coarse versus fine-grained stages based on dynamic context understanding, reinforcing the model’s adaptability to different text structures and types. Exploring inter-stage learning mechanisms and parameter sharing might yield efficiency improvements, particularly regarding computational resource utilization.

In conclusion, Summ$^N$ represents a substantive advancement in the summarization of lengthy texts, with implications that extend both theoretical foundations and practical methodologies in AI-driven text processing.