OpineSum: Entailment-based self-training for abstractive opinion
summarization
- URL: http://arxiv.org/abs/2212.10791v1
- Date: Wed, 21 Dec 2022 06:20:28 GMT
- Title: OpineSum: Entailment-based self-training for abstractive opinion
summarization
- Authors: Annie Louis and Joshua Maynez
- Abstract summary: We present a novel self-training approach, OpineSum, for abstractive opinion summarization.
The summaries in this approach are built using a novel application of textual entailment.
OpineSum achieves state-of-the-art performance in both settings.
- Score: 6.584115526134759
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A typical product or place often has hundreds of reviews, and summarization
of these texts is an important and challenging problem. Recent progress on
abstractive summarization in domains such as news has been driven by supervised
systems trained on hundreds of thousands of news articles paired with
human-written summaries. However for opinion texts, such large scale datasets
are rarely available. Unsupervised methods, self-training, and few-shot
learning approaches bridge that gap. In this work, we present a novel
self-training approach, OpineSum, for abstractive opinion summarization. The
summaries in this approach are built using a novel application of textual
entailment and capture the consensus of opinions across the various reviews for
an item. This method can be used to obtain silver-standard summaries on a large
scale and train both unsupervised and few-shot abstractive summarization
systems. OpineSum achieves state-of-the-art performance in both settings.
Related papers
- Factually Consistent Summarization via Reinforcement Learning with
Textual Entailment Feedback [57.816210168909286]
We leverage recent progress on textual entailment models to address this problem for abstractive summarization systems.
We use reinforcement learning with reference-free, textual entailment rewards to optimize for factual consistency.
Our results, according to both automatic metrics and human evaluation, show that our method considerably improves the faithfulness, salience, and conciseness of the generated summaries.
arXiv Detail & Related papers (2023-05-31T21:04:04Z) - On the State of German (Abstractive) Text Summarization [3.1776833268555134]
We assess the landscape of German abstractive text summarization.
We investigate why practically useful solutions for abstractive text summarization are still absent in industry.
arXiv Detail & Related papers (2023-01-17T18:59:20Z) - Generating Multiple-Length Summaries via Reinforcement Learning for
Unsupervised Sentence Summarization [44.835811239393244]
Sentence summarization shortens given texts while maintaining core contents of the texts.
Unsupervised approaches have been studied to summarize texts without human-written summaries.
We devise an abstractive model based on reinforcement learning without ground-truth summaries.
arXiv Detail & Related papers (2022-12-21T08:34:28Z) - Salience Allocation as Guidance for Abstractive Summarization [61.31826412150143]
We propose a novel summarization approach with a flexible and reliable salience guidance, namely SEASON (SaliencE Allocation as Guidance for Abstractive SummarizatiON)
SEASON utilizes the allocation of salience expectation to guide abstractive summarization and adapts well to articles in different abstractiveness.
arXiv Detail & Related papers (2022-10-22T02:13:44Z) - Learning Opinion Summarizers by Selecting Informative Reviews [81.47506952645564]
We collect a large dataset of summaries paired with user reviews for over 31,000 products, enabling supervised training.
The content of many reviews is not reflected in the human-written summaries, and, thus, the summarizer trained on random review subsets hallucinates.
We formulate the task as jointly learning to select informative subsets of reviews and summarizing the opinions expressed in these subsets.
arXiv Detail & Related papers (2021-09-09T15:01:43Z) - Neural Abstractive Unsupervised Summarization of Online News Discussions [1.2617078020344619]
We introduce a novel method that generates abstractive summaries of online news discussions.
Our model is evaluated using ROUGE scores between the generated summary and each comment on the thread.
arXiv Detail & Related papers (2021-06-07T20:33:51Z) - Summaformers @ LaySumm 20, LongSumm 20 [14.44754831438127]
In this paper, we look at the problem of summarizing scientific research papers from multiple domains.
We differentiate between two types of summaries, namely, LaySumm and LongSumm.
While leveraging latest Transformer-based models, our systems are simple, intuitive and based on how specific paper sections contribute to human summaries.
arXiv Detail & Related papers (2021-01-10T13:48:12Z) - Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised
Approach [89.56158561087209]
We study summarizing on arbitrary aspects relevant to the document.
Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme.
Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents.
arXiv Detail & Related papers (2020-10-14T03:20:46Z) - Few-Shot Learning for Opinion Summarization [117.70510762845338]
Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents.
In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text.
Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
arXiv Detail & Related papers (2020-04-30T15:37:38Z) - Unsupervised Opinion Summarization with Noising and Denoising [85.49169453434554]
We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof.
At test time, the model accepts genuine reviews and generates a summary containing salient opinions, treating those that do not reach consensus as noise.
arXiv Detail & Related papers (2020-04-21T16:54:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.