Loading…

GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization

Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, w...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Bao, Guangsheng, Ou, Zebin, Zhang, Yue
Format: Article
Language:English
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Bao, Guangsheng
Ou, Zebin
Zhang, Yue
description Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at \url{https://github.com/baoguangsheng/gemini}.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2900776594</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2900776594</sourcerecordid><originalsourceid>FETCH-proquest_journals_29007765943</originalsourceid><addsrcrecordid>eNqNyrsKwjAUgOEgCBbtOwScCzG9qZuUehl0aaFjieVUI2miyamoT6-CD-D0D98_IB4Pw1kwjzgfEd-5C2OMJymP49Aj1Sbf7w67Jc2MRmuUkvpE8Qy0AI2gGwgU3EHRykr8UoFPBbQ1lq6ODq1oUN6BlvBAWvRdJ6x8CZRGT8iwFcqB_-uYTNd5mW2DqzW3HhzWF9Nb_aGaLxhL0yReROF_1xsIxUFj</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2900776594</pqid></control><display><type>article</type><title>GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization</title><source>Publicly Available Content Database</source><creator>Bao, Guangsheng ; Ou, Zebin ; Zhang, Yue</creator><creatorcontrib>Bao, Guangsheng ; Ou, Zebin ; Zhang, Yue</creatorcontrib><description>Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at \url{https://github.com/baoguangsheng/gemini}.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Documents</subject><ispartof>arXiv.org, 2023-12</ispartof><rights>2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2900776594?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>777,781,25734,36993,44571</link.rule.ids></links><search><creatorcontrib>Bao, Guangsheng</creatorcontrib><creatorcontrib>Ou, Zebin</creatorcontrib><creatorcontrib>Zhang, Yue</creatorcontrib><title>GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization</title><title>arXiv.org</title><description>Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at \url{https://github.com/baoguangsheng/gemini}.</description><subject>Documents</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNyrsKwjAUgOEgCBbtOwScCzG9qZuUehl0aaFjieVUI2miyamoT6-CD-D0D98_IB4Pw1kwjzgfEd-5C2OMJymP49Aj1Sbf7w67Jc2MRmuUkvpE8Qy0AI2gGwgU3EHRykr8UoFPBbQ1lq6ODq1oUN6BlvBAWvRdJ6x8CZRGT8iwFcqB_-uYTNd5mW2DqzW3HhzWF9Nb_aGaLxhL0yReROF_1xsIxUFj</recordid><startdate>20231209</startdate><enddate>20231209</enddate><creator>Bao, Guangsheng</creator><creator>Ou, Zebin</creator><creator>Zhang, Yue</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20231209</creationdate><title>GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization</title><author>Bao, Guangsheng ; Ou, Zebin ; Zhang, Yue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_29007765943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Documents</topic><toplevel>online_resources</toplevel><creatorcontrib>Bao, Guangsheng</creatorcontrib><creatorcontrib>Ou, Zebin</creatorcontrib><creatorcontrib>Zhang, Yue</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bao, Guangsheng</au><au>Ou, Zebin</au><au>Zhang, Yue</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization</atitle><jtitle>arXiv.org</jtitle><date>2023-12-09</date><risdate>2023</risdate><eissn>2331-8422</eissn><abstract>Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at \url{https://github.com/baoguangsheng/gemini}.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2023-12
issn 2331-8422
language eng
recordid cdi_proquest_journals_2900776594
source Publicly Available Content Database
subjects Documents
title GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T00%3A43%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=GEMINI:%20Controlling%20the%20Sentence-level%20Writing%20Style%20for%20Abstractive%20Text%20Summarization&rft.jtitle=arXiv.org&rft.au=Bao,%20Guangsheng&rft.date=2023-12-09&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2900776594%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_29007765943%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2900776594&rft_id=info:pmid/&rfr_iscdi=true