Human-free quality estimation of document summaries
Project description
BLANC
This is the reference implementation of BLANC-help and BLANC-tune as defined in Fill in the BLANC: Human-free quality estimation of document summaries.
BLANC is a new approach to the automatic estimation of document summary quality. Our goal is to measure the functional performance of a summary with an objective, reproducible, and fully automated method. Our approach achieves this by measuring the performance boost gained by a pre-trained language model with access to a document summary while carrying out its language understanding task on the document's text. We present evidence that BLANC scores have at least as good correlation with human evaluations as do the ROUGE family of summary quality measurements. And unlike ROUGE, the BLANC method does not require human-written reference summaries, allowing for fully human-free summary quality estimation.
Setup
- Install Python 3.6 or higher
- Install with
pip install blanc
Note that we pinned the transformers
requirement to 2.4.0 due to Rust installation issues that are introduced in future versions. We used transformers
2.5.1 in our experiments, so you may want to try upgrading transformers
if you can.
Python Usage
Basic usage:
>>> from blanc import BlancHelp, BlancTune
>>> document = "Jack walked all the way over to go to the big market and buy a few loaves of bread for himself."
>>> summary = "Jack bought bread from the market"
>>> blanc_help = BlancHelp()
>>> blanc_tune = BlancTune()
>>> blanc_help.eval_once(document, summary)
0.14285714285714285
>>> blanc_tune.eval_once(document, summary)
0.14285714285714285
By default, BLANC is run on the CPU. Using CUDA with batching is much faster:
blanc_help = BlancHelp(device='cuda', inference_batch_size=128)
blanc_tune = BlancTune(device='cuda', inference_batch_size=24, finetune_batch_size=24)
With these batch sizes, BLANC-help takes around 1.4 sec per summary and BLANC-tune takes around 1.8 sec per summary on an NVIDIA V100. In addition to the parameters controlling device and batch sizes, BlancHelp and BlancTune take several other parameters controlling how the BLANC scores are calculated, and the default values for those parameters reproduce the results of the paper.
If you want to compute the BLANC scores of many documents and summaries at once, you can use eval_pairs()
or eval_summaries_for_docs()
. eval_pairs()
is useful when you have many documents, each with a single summary:
>>> documents = ["Jack walked all the way over to go to the big market and buy a few loaves of bread for himself.", "As Jill started taking a walk in the park, she certainly noticed that the trees were extra green this year."]
>>> summaries = ["Jack bought bread from the market.", "Jill saw green trees in the park."]
>>> blanc_help.eval_pairs(documents, summaries)
[0.14285714285714285, 0.07142857142857142]
eval_summaries_for_docs()
is useful when you have many documents, each with many summaries:
>>> blanc_tune.eval_summaries_for_docs(documents, doc_summaries)
[[0.14285714285714285, 0.14285714285714285], [0.14285714285714285, 0.07142857142857142]]
CLI Usage
A CLI for computing BLANC scores is provided for convenience.
$ blanc help --doc "Jack walked all the way over to go to the big market and buy a few loaves of bread for himself." --summary "Jack bought bread from the market."
0.14285714285714285
Input data can also be provided in JSON format, with sample JSON input provided in data/
$ blanc help --single_json data/single.json
0.14285714285714285
$ blanc tune --pairs_json data/pairs.json
[0.2857142857142857, 0.21428571428571427]
$ blanc tune --doc_summaries_json data/doc-summaries.json
[[0.2857142857142857, 0.14285714285714285], [0.14285714285714285, 0.14285714285714285]]
The single_json
input format expects a single JSON blob with keys document
and summary
. The pairs_json
input format expects a list of JSON blobs, each with a document
and a summary
. The doc_summaries_json
input format expects a list of JSON blobs, each with keys document
and summaries
, where summaries
is a list of strings. These keys are customizable with the doc_key
, summary_key
, and summaries_key
arguments. By default, the output is printed to STDOUT, but it can be written to a JSON file provided with the output_json
argument.
Full documentation is available with blanc --help
:
required arguments:
{help,tune} BLANC-help or BLANC-tune
input arguments:
--doc DOC single input document (default: None)
--summary SUMMARY single input summary (default: None)
--single_json FILENAME
filename for single document summary pair (default:
None)
--pairs_json FILENAME
filename for list of document summary pairs (default:
None)
--doc_summaries_json FILENAME
filename for list of documents, each with a list of
summaries (default: None)
--doc_key KEY json key for the input document (default: doc)
--summary_key KEY json key for the input summary (single_json or
pairs_json input) (default: summary)
--summaries_key KEY json key for the input summaries (doc_summaries_json
input) (default: summaries)
arguments for BLANC-help and BLANC-tune:
--model_name NAME BERT model type (default: bert-base-uncased)
--measure {improve,relative}
measure improve or relative, as defined in the paper
(default: relative)
--gap GAP distance between words to mask during inference
(default: 6)
--min_token_length_normal LEN
minimum number of chars in normal tokens to mask,
where a normal token is a whole word (default: 4)
--min_token_length_lead LEN
minimum number of chars in lead token to mask, where a
lead token begins a word (default: 2)
--min_token_length_followup LEN
minimum number of chars in followup token to mask,
where a followup token continues a word (default: 100)
--device DEVICE cpu or cuda device (default: cpu)
--random_seed SEED random seed for python and torch (default: 1)
--inference_batch_size SIZE
batch size to use during inference (default: 1)
--inference_mask_evenly MASK_EVENLY
when True, mask every `gap` tokens that are longer
than `min_token_length`during finetuning, when False
randomly mask tokens with probability 0.15 (default:
True)
BLANC-help arguments:
--filler_token TOKEN token to use as filler in lieu of summary (default: .)
--help_sep SEP token to use to separate the summary or filler from
the sentence, or '' for no separator (default: )
BLANC-tune arguments:
--finetune_batch_size SIZE
batch size to use when finetuning on summary (default:
1)
--finetune_epochs EPOCHS
number of epochs to train for when finetuning on
summary (default: 10)
--finetune_mask_evenly MASK_EVENLY
when True, mask every `gap` tokens that are longer
than `min_token_length`during finetuning, when False
randomly mask tokens with probability 0.15 (default:
False)
--finetune_chunk_size SIZE
number of summary tokens to use at a time when
finetuning (default: 64)
--finetune_chunk_stride STRIDE
number of tokens between summary chunks for finetuning
(default: 32)
--learning_rate LR learning rate when finetuning on summary (default:
5e-05)
--warmup_steps STEPS warmup steps when finetuning on summary (default: 0)
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