Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization

• URL: http://arxiv.org/abs/2306.04787v1
• Date: Wed, 7 Jun 2023 21:18:23 GMT
• Title: Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization
• Authors: Mohamed Trabelsi and Huseyin Uzunalioglu
• Abstract summary: Multi-document summarization (MDS) refers to the task of summarizing the text in multiple documents into a concise summary. Abstractive MDS aims to generate a coherent and fluent summary for multiple documents using natural language generation techniques. We propose Absformer, a new Transformer-based method for unsupervised abstractive summary generation.
• Score: 1.066048003460524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-document summarization (MDS) refers to the task of summarizing the text in multiple documents into a concise summary. The generated summary can save the time of reading many documents by providing the important content in the form of a few sentences. Abstractive MDS aims to generate a coherent and fluent summary for multiple documents using natural language generation techniques. In this paper, we consider the unsupervised abstractive MDS setting where there are only documents with no groundtruh summaries provided, and we propose Absformer, a new Transformer-based method for unsupervised abstractive summary generation. Our method consists of a first step where we pretrain a Transformer-based encoder using the masked language modeling (MLM) objective as the pretraining task in order to cluster the documents into semantically similar groups; and a second step where we train a Transformer-based decoder to generate abstractive summaries for the clusters of documents. To our knowledge, we are the first to successfully incorporate a Transformer-based model to solve the unsupervised abstractive MDS task. We evaluate our approach using three real-world datasets from different domains, and we demonstrate both substantial improvements in terms of evaluation metrics over state-of-the-art abstractive-based methods, and generalization to datasets from different domains.

Related papers

• Unified Multi-Modal Interleaved Document Representation for Information Retrieval [57.65409208879344]
  We produce more comprehensive and nuanced document representations by holistically embedding documents interleaved with different modalities. Specifically, we achieve this by leveraging the capability of recent vision-language models that enable the processing and integration of text, images, and tables into a unified format and representation.
  arXiv  Detail & Related papers  (2024-10-03T17:49:09Z)
• Write Summary Step-by-Step: A Pilot Study of Stepwise Summarization [48.57273563299046]
  We propose the task of Stepwise Summarization, which aims to generate a new appended summary each time a new document is proposed. The appended summary should not only summarize the newly added content but also be coherent with the previous summary. We show that SSG achieves state-of-the-art performance in terms of both automatic metrics and human evaluations.
  arXiv  Detail & Related papers  (2024-06-08T05:37:26Z)
• The Power of Summary-Source Alignments [62.76959473193149]
  Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection. alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training data. This paper proposes extending the summary-source alignment framework by applying it at the more fine-grained proposition span level.
  arXiv  Detail & Related papers  (2024-06-02T19:35:19Z)
• Meta-Task Prompting Elicits Embeddings from Large Language Models [54.757445048329735]
  We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation. We generate high-quality sentence embeddings from Large Language Models without the need for model fine-tuning. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.
  arXiv  Detail & Related papers  (2024-02-28T16:35:52Z)
• Generating a Structured Summary of Numerous Academic Papers: Dataset and Method [20.90939310713561]
  We propose BigSurvey, the first large-scale dataset for generating comprehensive summaries of numerous academic papers on each topic. We collect target summaries from more than seven thousand survey papers and utilize their 430 thousand reference papers' abstracts as input documents. To organize the diverse content from dozens of input documents, we propose a summarization method named category-based alignment and sparse transformer (CAST)
  arXiv  Detail & Related papers  (2023-02-09T11:42:07Z)
• ACM -- Attribute Conditioning for Abstractive Multi Document Summarization [0.0]
  We propose a model that incorporates attribute conditioning modules in order to decouple conflicting information by conditioning for a certain attribute in the output summary. This approach shows strong gains in ROUGE score over baseline multi document summarization approaches.
  arXiv  Detail & Related papers  (2022-05-09T00:00:14Z)
• Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization [18.791701342934605]
  The Query Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on a given query. A key challenge in addressing this task is the lack of large labeled data for training the summarization model. We address this challenge by exploring a series of domain adaptation techniques.
  arXiv  Detail & Related papers  (2021-12-22T05:34:56Z)
• HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles [8.53502615629675]
  We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS) This use-case is different from the use-cases covered in existing multi-document summarization (MDS) datasets and is applicable to educational and industrial scenarios. We describe the creation of the dataset and discuss the unique features that distinguish it from other summarization corpora.
  arXiv  Detail & Related papers  (2021-10-07T04:44:32Z)
• WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization [16.048329028104643]
  In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents. One major challenge for this task is the lack of availability of labeled training datasets. We propose a novel weakly supervised learning approach via utilizing distant supervision.
  arXiv  Detail & Related papers  (2020-11-03T02:02:55Z)
• SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression [61.97200991151141]
  SummPip is an unsupervised method for multi-document summarization. We convert the original documents to a sentence graph, taking both linguistic and deep representation into account. We then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary.
  arXiv  Detail & Related papers  (2020-07-17T13:01:15Z)
• Pre-training via Paraphrasing [96.79972492585112]
  We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual paraphrasing objective. We show it is possible to jointly learn to do retrieval and reconstruction, given only a random initialization. For example, with no additional task-specific training we achieve BLEU scores of up to 35.8 for document translation.
  arXiv  Detail & Related papers  (2020-06-26T14:43:43Z)

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.