HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information

• URL: http://arxiv.org/abs/2203.09629v1
• Date: Thu, 17 Mar 2022 21:49:26 GMT
• Title: HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information
• Authors: Qian Ruan, Malte Ostendorff, Georg Rehm
• Abstract summary: We propose a novel approach to formulate, extract, encode and inject hierarchical structure information explicitly into an extractive summarization model. Using various experimental settings on three datasets (i.e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively.
• Score: 0.6443952406204634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy. In addition, section titles usually indicate the common topic of their respective sentences. We propose a novel approach to formulate, extract, encode and inject hierarchical structure information explicitly into an extractive summarization model based on a pre-trained, encoder-only Transformer language model (HiStruct+ model), which improves SOTA ROUGEs for extractive summarization on PubMed and arXiv substantially. Using various experimental settings on three datasets (i.e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively, which differs from our model only in that the hierarchical structure information is not injected. It is also observed that the more conspicuous hierarchical structure the dataset has, the larger improvements our method gains. The ablation study demonstrates that the hierarchical position information is the main contributor to our model's SOTA performance.

Related papers

• SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding [55.48936731641802]
  We present the SRFUND, a hierarchically structured multi-task form understanding benchmark. SRFUND provides refined annotations on top of the original FUNSD and XFUND datasets. The dataset includes eight languages including English, Chinese, Japanese, German, French, Spanish, Italian, and Portuguese.
  arXiv  Detail & Related papers  (2024-06-13T02:35:55Z)
• Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically [74.96551626420188]
  Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures. We investigate sources of inductive bias in transformer models and their training that could cause such generalization behavior to emerge.
  arXiv  Detail & Related papers  (2024-04-25T07:10:29Z)
• Grokking of Hierarchical Structure in Vanilla Transformers [72.45375959893218]
  We show that transformer language models can learn to generalize hierarchically after training for extremely long periods. intermediate-depth models generalize better than both very deep and very shallow transformers.
  arXiv  Detail & Related papers  (2023-05-30T04:34:13Z)
• Implant Global and Local Hierarchy Information to Sequence based Code Representation Models [25.776540440893257]
  We analyze how the complete hierarchical structure influences the tokens in code sequences and abstract this influence as a property of code tokens called hierarchical embedding. We propose the Hierarchy Transformer (HiT), a simple but effective sequence model to incorporate the complete hierarchical embeddings of source code into a Transformer model.
  arXiv  Detail & Related papers  (2023-03-14T12:01:39Z)
• Structured information extraction from complex scientific text with fine-tuned large language models [55.96705756327738]
  We present a simple sequence-to-sequence approach to joint named entity recognition and relation extraction. The approach leverages a pre-trained large language model (LLM), GPT-3, that is fine-tuned on approximately 500 pairs of prompts. This approach represents a simple, accessible, and highly-flexible route to obtaining large databases of structured knowledge extracted from unstructured text.
  arXiv  Detail & Related papers  (2022-12-10T07:51:52Z)
• Abstractive Summarization Guided by Latent Hierarchical Document Structure [28.284926421845533]
  Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences. We propose a hierarchy-aware graph neural network (HierGNN) which captures such dependencies through three main steps. Experiments confirm HierGNN improves strong sequence models such as BART, with a 0.55 and 0.75 margin in average ROUGE-1/2/L for CNN/DM and XSum.
  arXiv  Detail & Related papers  (2022-11-17T11:02:30Z)
• Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT [9.271716501646194]
  We propose to extract fact-level semantic units for better extractive summarization. We incorporate our model with BERT using a hierarchical graph mask. Experiments on the CNN/DaliyMail dataset show that our model achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2020-11-19T09:29:51Z)
• Extractive Summarization as Text Matching [123.09816729675838]
  This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. We formulate the extractive summarization task as a semantic text matching problem. We have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1)
  arXiv  Detail & Related papers  (2020-04-19T08:27:57Z)
• A Hierarchical Transformer for Unsupervised Parsing [0.0]
  We extend the transformer model by enabling it to learn hierarchical representations. We train our new model on language modelling and then apply it to the task of unsupervised parsing. We achieve reasonable results on the freely available subset of the WSJ10 dataset with an F1-score of about 50%.
  arXiv  Detail & Related papers  (2020-03-30T22:07:22Z)
• StructSum: Summarization via Structured Representations [27.890477913486787]
  Abstractive text summarization aims at compressing the information of a long source document into a condensed summary. Despite advances in modeling techniques, abstractive summarization models still suffer from several key challenges. We propose a framework based on document-level structure induction for summarization to address these challenges.
  arXiv  Detail & Related papers  (2020-03-01T20:32:51Z)
• Tree-structured Attention with Hierarchical Accumulation [103.47584968330325]
  "Hierarchical Accumulation" encodes parse tree structures into self-attention at constant time complexity. Our approach outperforms SOTA methods in four IWSLT translation tasks and the WMT'14 English-German translation task.
  arXiv  Detail & Related papers  (2020-02-19T08:17:00Z)

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.