Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees

• URL: http://arxiv.org/abs/2209.10492v1
• Date: Wed, 21 Sep 2022 16:50:22 GMT
• Title: Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
• Authors: Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal
• Abstract summary: Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales. We propose the Summarization Program (SP), an interpretable modular framework consisting of an (ordered) list of binary trees. A Summarization Program contains one root node per summary sentence, and a distinct tree connects each summary sentence to the document sentences.
• Score: 89.60269205320431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization Program (SP), an interpretable modular framework consisting of an (ordered) list of binary trees, each encoding the step-by-step generative process of an abstractive summary sentence from the source document. A Summarization Program contains one root node per summary sentence, and a distinct tree connects each summary sentence (root node) to the document sentences (leaf nodes) from which it is derived, with the connecting nodes containing intermediate generated sentences. Edges represent different modular operations involved in summarization such as sentence fusion, compression, and paraphrasing. We first propose an efficient best-first search method over neural modules, SP-Search that identifies SPs for human summaries by directly optimizing for ROUGE scores. Next, using these programs as automatic supervision, we propose seq2seq models that generate Summarization Programs, which are then executed to obtain final summaries. We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior. We also conduct a simulation study to show that Summarization Programs improve the interpretability of summarization models by allowing humans to better simulate model reasoning. Summarization Programs constitute a promising step toward interpretable and modular abstractive summarization, a complex task previously addressed primarily through blackbox end-to-end neural systems. Our code is available at https://github.com/swarnaHub/SummarizationPrograms

Related papers

• GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization [13.61818620609812]
  We propose a lightweight yet effective unsupervised approach called GLIMMER: a Graph and LexIcal features based unsupervised Multi-docuMEnt summaRization approach. It first constructs a sentence graph from the source documents, then automatically identifies semantic clusters by mining low-level features from raw texts. Experiments conducted on Multi-News, Multi-XScience and DUC-2004 demonstrate that our approach outperforms existing unsupervised approaches.
  arXiv  Detail & Related papers  (2024-08-19T16:01:48Z)
• Hierarchical Indexing for Retrieval-Augmented Opinion Summarization [60.5923941324953]
  We propose a method for unsupervised abstractive opinion summarization that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs) Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews.
  arXiv  Detail & Related papers  (2024-03-01T10:38:07Z)
• Source Identification in Abstractive Summarization [0.8883733362171033]
  We define input sentences that contain essential information in the generated summary as $textitsource sentences$ and study how abstractive summaries are made by analyzing the source sentences. We formulate automatic source sentence detection and compare multiple methods to establish a strong baseline for the task. Experimental results show that the perplexity-based method performs well in highly abstractive settings, while similarity-based methods robustly in relatively extractive settings.
  arXiv  Detail & Related papers  (2024-02-07T09:09:09Z)
• AugSumm: towards generalizable speech summarization using synthetic labels from large language model [61.73741195292997]
  Abstractive speech summarization (SSUM) aims to generate human-like summaries from speech. conventional SSUM models are mostly trained and evaluated with a single ground-truth (GT) human-annotated deterministic summary. We propose AugSumm, a method to leverage large language models (LLMs) as a proxy for human annotators to generate augmented summaries.
  arXiv  Detail & Related papers  (2024-01-10T18:39:46Z)
• An Extractive-and-Abstractive Framework for Source Code Summarization [28.553366270065656]
  Code summarization aims to automatically generate summaries/comments for a given code snippet in the form of natural language. We propose a novel extractive-and-abstractive framework to generate human-written-like summaries with preserved factual details.
  arXiv  Detail & Related papers  (2022-06-15T02:14:24Z)
• Reinforcing Semantic-Symmetry for Document Summarization [15.113768658584979]
  Document summarization condenses a long document into a short version with salient information and accurate semantic descriptions. This paper introduces a new textbfreinforcing stextbfemantic-textbfsymmetry learning textbfmodel is proposed for document summarization. A series of experiments have been conducted on two wildly used benchmark datasets CNN/Daily Mail and BigPatent.
  arXiv  Detail & Related papers  (2021-12-14T17:41:37Z)
• Representing Partial Programs with Blended Abstract Semantics [62.20775388513027]
  We introduce a technique for representing partially written programs in a program synthesis engine. We learn an approximate execution model implemented as a modular neural network. We show that these hybrid neuro-symbolic representations enable execution-guided synthesizers to use more powerful language constructs.
  arXiv  Detail & Related papers  (2020-12-23T20:40:18Z)
• 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)
• Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward [42.925345819778656]
  We present ASGARD, a novel framework for Abstractive Summarization with Graph-Augmentation and semantic-driven RewarD. We propose the use of dual encoders---a sequential document encoder and a graph-structured encoder---to maintain the global context and local characteristics of entities. Results show that our models produce significantly higher ROUGE scores than a variant without knowledge graph as input on both New York Times and CNN/Daily Mail datasets.
  arXiv  Detail & Related papers  (2020-05-03T18:23:06Z)
• 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)

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.