48,347 research outputs found
Fair Abstractive Summarization of Diverse Perspectives
People from different social and demographic groups express diverse
perspectives and conflicting opinions on a broad set of topics such as product
reviews, healthcare, law, and politics. A fair summary should provide a
comprehensive coverage of diverse perspectives without underrepresenting
certain groups. However, current work in summarization metrics and Large
Language Models (LLMs) evaluation has not explored fair abstractive
summarization. In this paper, we systematically investigate fair abstractive
summarization for user-generated data. We first formally define fairness in
abstractive summarization as not underrepresenting perspectives of any groups
of people and propose four reference-free automatic metrics measuring the
differences between target and source perspectives. We evaluate five LLMs,
including three GPT models, Alpaca, and Claude, on six datasets collected from
social media, online reviews, and recorded transcripts. Experiments show that
both the model-generated and the human-written reference summaries suffer from
low fairness. We conduct a comprehensive analysis of the common factors
influencing fairness and propose three simple but effective methods to
alleviate unfair summarization. Our dataset and code are available at
https://github.com/psunlpgroup/FairSumm.Comment: 19 pages, 10 figure
A Neural Model for Generating Natural Language Summaries of Program Subroutines
Source code summarization -- creating natural language descriptions of source
code behavior -- is a rapidly-growing research topic with applications to
automatic documentation generation, program comprehension, and software
maintenance. Traditional techniques relied on heuristics and templates built
manually by human experts. Recently, data-driven approaches based on neural
machine translation have largely overtaken template-based systems. But nearly
all of these techniques rely almost entirely on programs having good internal
documentation; without clear identifier names, the models fail to create good
summaries. In this paper, we present a neural model that combines words from
code with code structure from an AST. Unlike previous approaches, our model
processes each data source as a separate input, which allows the model to learn
code structure independent of the text in code. This process helps our approach
provide coherent summaries in many cases even when zero internal documentation
is provided. We evaluate our technique with a dataset we created from 2.1m Java
methods. We find improvement over two baseline techniques from SE literature
and one from NLP literature
Towards Automatic Generation of Short Summaries of Commits
Committing to a version control system means submitting a software change to
the system. Each commit can have a message to describe the submission. Several
approaches have been proposed to automatically generate the content of such
messages. However, the quality of the automatically generated messages falls
far short of what humans write. In studying the differences between
auto-generated and human-written messages, we found that 82% of the
human-written messages have only one sentence, while the automatically
generated messages often have multiple lines. Furthermore, we found that the
commit messages often begin with a verb followed by an direct object. This
finding inspired us to use a "verb+object" format in this paper to generate
short commit summaries. We split the approach into two parts: verb generation
and object generation. As our first try, we trained a classifier to classify a
diff to a verb. We are seeking feedback from the community before we continue
to work on generating direct objects for the commits.Comment: 4 pages, accepted in ICPC 2017 ERA Trac
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
We study unsupervised multi-document summarization evaluation metrics, which
require neither human-written reference summaries nor human annotations (e.g.
preferences, ratings, etc.). We propose SUPERT, which rates the quality of a
summary by measuring its semantic similarity with a pseudo reference summary,
i.e. selected salient sentences from the source documents, using contextualized
embeddings and soft token alignment techniques. Compared to the
state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with
human ratings by 18-39%. Furthermore, we use SUPERT as rewards to guide a
neural-based reinforcement learning summarizer, yielding favorable performance
compared to the state-of-the-art unsupervised summarizers. All source code is
available at https://github.com/yg211/acl20-ref-free-eval.Comment: ACL 202
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