Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries

• URL: http://arxiv.org/abs/2010.07074v2
• Date: Fri, 9 Sep 2022 15:27:55 GMT
• Title: Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries
• Authors: Xiaofei Sun, Zijun Sun, Yuxian Meng, Jiwei Li and Chun Fan
• Abstract summary: We propose SOE, a pipelined system that outlines, outlining and elaborating for long text generation. SOE produces long texts with significantly better quality, along with faster convergence speed.
• Score: 46.183289748907804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The difficulty of generating coherent long texts lies in the fact that existing models overwhelmingly focus on predicting local words, and cannot make high level plans on what to generate or capture the high-level discourse dependencies between chunks of texts. Inspired by human writing processes, where a list of bullet points or a catalog is first outlined, and then each bullet point is expanded to form the whole article, we propose {\it SOE}, a pipelined system that involves of summarizing, outlining and elaborating for long text generation: the model first outlines the summaries for different segments of long texts, and then elaborates on each bullet point to generate the corresponding segment. To avoid the labor-intensive process of summary soliciting, we propose the {\it reconstruction} strategy, which extracts segment summaries in an unsupervised manner by selecting its most informative part to reconstruct the segment. The proposed generation system comes with the following merits: (1) the summary provides high-level guidance for text generation and avoids the local minimum of individual word predictions; (2) the high-level discourse dependencies are captured in the conditional dependencies between summaries and are preserved during the summary expansion process and (3) additionally, we are able to consider significantly more contexts by representing contexts as concise summaries. Extensive experiments demonstrate that SOE produces long texts with significantly better quality, along with faster convergence speed.

Related papers

• 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)
• A General Contextualized Rewriting Framework for Text Summarization [15.311467109946571]
  Exiting rewriting systems take each extractive sentence as the only input, which is relatively focused but can lose necessary background knowledge and discourse context. We formalize contextualized rewriting as a seq2seq with group-tag alignments, identifying extractive sentences through content-based addressing. Results show that our approach significantly outperforms non-contextualized rewriting systems without requiring reinforcement learning.
  arXiv  Detail & Related papers  (2022-07-13T03:55:57Z)
• A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization [18.763290930749235]
  summarization is the process of shortening a set of textual data computationally, to create a subset (a summary) Existing summarization methods can be roughly divided into two types: extractive and abstractive. An extractive summarizer explicitly selects text snippets from the source document, while an abstractive summarizer generates novel text snippets to convey the most salient concepts prevalent in the source.
  arXiv  Detail & Related papers  (2022-04-20T14:56:36Z)
• Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents [13.755637074366813]
  SummN is a simple, flexible, and effective multi-stage framework for input texts longer than the maximum context lengths of typical pretrained LMs. It can process input text of arbitrary length by adjusting the number of stages while keeping the LM context size fixed. Our experiments demonstrate that SummN significantly outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-16T06:19:54Z)
• Text Summarization with Latent Queries [60.468323530248945]
  We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms. Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time. Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
  arXiv  Detail & Related papers  (2021-05-31T21:14:58Z)
• Controllable Abstractive Dialogue Summarization with Sketch Supervision [56.59357883827276]
  Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score.
  arXiv  Detail & Related papers  (2021-05-28T19:05:36Z)
• Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence [59.51720326054546]
  We propose a long text generation model, which can represent the prefix sentences at sentence level and discourse level in the decoding process. Our model can generate more coherent texts than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-05-19T07:29:08Z)
• Screenplay Summarization Using Latent Narrative Structure [78.45316339164133]
  We propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models. We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays. Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode.
  arXiv  Detail & Related papers  (2020-04-27T11:54:19Z)
• 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)
• The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries [72.48439126769627]
  We introduce the Shmoop Corpus: a dataset of 231 stories paired with detailed multi-paragraph summaries for each individual chapter. From the corpus, we construct a set of common NLP tasks, including Cloze-form question answering and a simplified form of abstractive summarization. We believe that the unique structure of this corpus provides an important foothold towards making machine story comprehension more approachable.
  arXiv  Detail & Related papers  (2019-12-30T21:03:59Z)

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.