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SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization

TL;DR

SummaReranker improves abstractive summarization by 5.44% ROUGE-1 on CNN/DM using a multi-task mixture-of-experts re-ranking framework.

cs.CL 🔴 Advanced 2022-03-13 34 views
Mathieu Ravaut Shafiq Joty Nancy F. Chen
AI Reader Arxiv Page Download PDF
abstractive summarization re-ranking multi-task learning mixture-of-experts NLP

Key Findings

Methodology

SummaReranker is a two-stage framework leveraging a mixture-of-experts model and multi-task learning to optimize multiple metrics (e.g., ROUGE, BERTScore). It re-ranks candidates to select the best summary.

Key Results

  • On CNN/DM, SummaReranker improves PEGASUS ROUGE-1 from 44.23 to 47.16 (+5.44%).
  • On XSum, ROUGE-1 improves from 47.33 to 48.12 (+1.31%).
  • On Reddit TIFU, ROUGE-1 improves from 26.63 to 29.83 (+9.34%).

Significance

This work addresses the limitations of current generative models in selecting optimal summaries, significantly improving quality in domains like news and social media.

Technical Contribution

Introduces a multi-task mixture-of-experts model to jointly optimize multiple evaluation metrics; combines diverse decoding methods (e.g., beam search, top-k sampling) for efficient candidate selection.

Novelty

First to integrate mixture-of-experts with multi-task learning for summary re-ranking, achieving significant performance gains over state-of-the-art methods.

Limitations

  • Relies on multiple decoding methods, increasing computational cost.
  • Performance may degrade under training-inference distribution mismatch.
  • Applicability to low-resource datasets remains unverified.

Future Work

Future work could explore more efficient candidate generation, improve low-resource applicability, and reduce training-inference distribution gaps.

AI Executive Summary

Existing abstractive summarization models often rely on beam search to generate a single summary, but large search spaces and exposure bias limit their quality. SummaReranker proposes a two-stage framework using a mixture-of-experts model and multi-task learning to re-rank candidate summaries, achieving significant quality improvements.

Built on pre-trained models like PEGASUS and BART, SummaReranker generates candidates using diverse decoding methods and optimizes metrics like ROUGE and BERTScore to select the best summary. On CNN/DM, it improves ROUGE-1 by 5.44%, with similar gains on XSum and Reddit TIFU datasets.

Despite higher computational costs, SummaReranker offers a novel and effective solution for abstractive summarization, particularly in news and social media. Future research could optimize efficiency and expand its applicability to broader domains.

Deep Analysis

Background

Abstractive summarization has advanced significantly with pre-trained models like BART and PEGASUS. However, these models often rely on beam search, which struggles to fully utilize candidate diversity.

Core Problem

Current methods suffer from exposure bias and suboptimal candidate selection. The challenge lies in identifying the best summary from diverse candidates generated by multiple decoding methods.

Innovation

SummaReranker combines a mixture-of-experts architecture with multi-task learning to optimize multiple metrics simultaneously. It integrates diverse decoding methods to improve candidate selection efficiency and quality.

Methodology

  • �� Generate candidate summaries using PEGASUS/BART.
  • �� Employ a mixture-of-experts model with multi-task learning to optimize ROUGE and BERTScore.
  • �� Re-rank candidates in a two-stage framework to select the best summary.

Experiments

Experiments were conducted on CNN/DM, XSum, and Reddit TIFU datasets using PEGASUS and BART as baselines. Metrics like ROUGE and BERTScore were used to evaluate improvements.

Results

SummaReranker improves ROUGE-1 on CNN/DM by 5.44%, with gains of 1.31% on XSum and 9.34% on Reddit TIFU, outperforming state-of-the-art methods.

Applications

Applicable to news summarization, social media content extraction, and other domains requiring high-quality abstractive summaries.

Limitations & Outlook

High computational cost, sensitivity to training-inference distribution mismatch, and untested performance on low-resource datasets.

Plain Language Accessible to non-experts

Think of SummaReranker as a 'judging panel' for summaries. Each candidate summary is like a contestant, and the judges score them based on criteria like clarity and informativeness. The panel, made up of experts, combines their scores to pick the best contestant. This ensures the chosen summary is the most well-rounded.

ELI14 Explained like you're 14

Imagine you're in a school talent show, and judges are picking the winner. Each act gets scored on things like creativity and skill. SummaReranker is like a super-smart judge that looks at all the acts and picks the best one based on multiple criteria. Cool, right? It makes sure the winner really deserves it!

Glossary

Abstractive Summarization

A generative summarization technique that creates new sentences to summarize content, rather than extracting from the source.

Used to generate candidate summaries in this paper.

Mixture-of-Experts

A model architecture where multiple expert modules collaborate, each specializing in a specific task.

Used to optimize multiple evaluation metrics in re-ranking.

ROUGE

A summarization evaluation metric that measures overlap between generated and reference summaries.

Used to assess summary quality.

BERTScore

A metric based on BERT embeddings that evaluates semantic similarity between texts.

Supplementary to ROUGE in this study.

Exposure Bias

A discrepancy where models are trained on true data but infer on generated data, leading to errors.

Discussed as a limitation in summarization models.

Open Questions Unanswered questions from this research

  • 1 How can candidate generation be made more computationally efficient?
  • 2 What techniques can improve performance on low-resource datasets?
  • 3 How can training-inference distribution mismatch be minimized?

Applications

Immediate Applications

News Summarization

Enhances summary quality and coverage for news media and aggregation platforms.

Social Media Content Extraction

Generates concise, informative summaries for social media users, improving engagement.

Long-term Vision

General Text Generation

Extends to broader tasks like dialogue generation and machine translation, improving output quality.

Abstract

Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the downstream dataset. These models are typically decoded with beam search to generate a unique summary. However, the search space is very large, and with the exposure bias, such decoding is not optimal. In this paper, we show that it is possible to directly train a second-stage model performing re-ranking on a set of summary candidates. Our mixture-of-experts SummaReranker learns to select a better candidate and consistently improves the performance of the base model. With a base PEGASUS, we push ROUGE scores by 5.44% on CNN-DailyMail (47.16 ROUGE-1), 1.31% on XSum (48.12 ROUGE-1) and 9.34% on Reddit TIFU (29.83 ROUGE-1), reaching a new state-of-the-art. Our code and checkpoints will be available at https://github.com/ntunlp/SummaReranker.

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