controlled_summarization.py

from typing import List, Tuple
import torch
from SciAssist import Summarization
import os
import requests
from datasets import load_dataset
print(f"Is CUDA available: {torch.cuda.is_available()}")
# True
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")

acl_data = load_dataset("dyxohjl666/CocoScisum_ACL", revision="refs/convert/parquet")
device = "gpu" if torch.cuda.is_available() else "cpu"

ctrlsum_pipeline = Summarization(os_name="nt",model_name="flan-t5-xl",checkpoint="dyxohjl666/flant5-xl-cocoscisum",device=device)

acl_dict = {}
recommended_kw = {}


def convert_to_dict(data):
    """ Dict:
        { url:
            {length:
                {keywords: summary};
             raw_text:
                 str;
            }
        }

    """
    url = data["url"]
    text = data["text"]
    keywords = data["keywords"]
    length = data["length"]
    summary = data["summary"]
    for u, t, k, l, s in zip(url, text, keywords, length, summary):
        if len(u) < 5:
            continue
        u = u + ".pdf"
        if k == None:
            k = ""
        if l == None:
            l = ""
        k = str(k).strip()
        l = str(l).strip()
        if u in acl_dict.keys():
            if k in acl_dict[u][l].keys():
                continue
            else:
                acl_dict[u][l][k] = s
        else:
            acl_dict[u] = {"": {}, "50": {}, "100": {}, "200": {}, "raw_text": t}

        # kws
        if u in recommended_kw.keys():
            if k == "" or k in recommended_kw[u]:
                continue
            else:
                recommended_kw[u].append(k)
        else:
            recommended_kw[u] = []
    return 1


for i in acl_data.keys():
    signal = convert_to_dict(acl_data[i])


def download_pdf(url, dest_folder):
    """
    Download a PDF from a given URL and save it to a specified destination folder.
    Parameters:
        url (str): URL of the PDF
        dest_folder (str): Destination folder to save the downloaded PDF
    """

    if not os.path.exists(dest_folder):
        os.makedirs(dest_folder)

    response = requests.get(url, stream=True)
    filename = os.path.join(dest_folder, url.split("/")[-1])

    with open(filename, 'wb') as file:
        for chunk in response.iter_content(chunk_size=1024):
            if chunk:
                file.write(chunk)
    print(f"Downloaded {url} to {filename}")
    return filename


def ctrlsum_for_str(input, length=None, keywords=None) -> List[Tuple[str, str]]:
    if keywords is not None:
        keywords = keywords.strip().split(",")
        if keywords[0] == "":
            keywords = None
    if length == 0 or length is None:
        length = None
    results = ctrlsum_pipeline.predict(input, type="str",
                                       length=length, keywords=keywords, num_beams=1)

    output = []
    for res in results["summary"]:
        output.append(f"{res}\n\n")
    return "".join(output)


def ctrlsum_for_file(input=None, length=None, keywords="", text="", url="") -> List[Tuple[str, str, str]]:
    if input == None and url == "":
        if text == "":
            return None, "Input cannot be left blank.", None
        else:
            return ctrlsum_for_str(text, length, keywords), text, None
    else:
        filename = ""
        url = url.strip()
        if url != "":
            if len(url) > 4 and url[-3:] == "pdf":
                if url.strip() in acl_dict.keys():
                    raw_text = acl_dict[url]["raw_text"]
                    l = str(length)
                    if length == 0:
                        l = ""
                    if l in acl_dict[url].keys():
                        if keywords.strip() in acl_dict[url][l].keys():
                            summary = acl_dict[url][l][keywords]
                            return summary, raw_text, None
                    if keywords.strip() == "":
                        keywords = None
                    if l == "":
                        l = None
                    return ctrlsum_for_str(raw_text, int(l), keywords), raw_text, None

                filename = download_pdf(url, './cache/')
            else:
                "Invalid url(Not PDF)!", None, None
        else:
            filename = input.name
        if keywords != "":
            keywords = keywords.strip().split(",")
            if keywords[0] == "":
                keywords = None
        if length == 0:
            length = None
        # Identify the format of input and parse reference strings
        if filename[-4:] == ".txt":
            results = ctrlsum_pipeline.predict(filename, type="txt",
                                               save_results=False,
                                               length=length, keywords=keywords, num_beams=1)
        elif filename[-4:] == ".pdf":
            results = ctrlsum_pipeline.predict(filename,
                                               save_results=False, length=length, keywords=keywords, num_beams=1)
        else:
            return "File Format Error !", None, filename

        output = []
        for res in results["summary"]:
            output.append(f"{res}\n\n")
        return "".join(output), results["raw_text"], filename


ctrlsum_str_example = "Language model pre-training has been shown to be effective for improving many natural language processing tasks ( Dai and Le , 2015 ; Peters et al. , 2018a ; Radford et al. , 2018 ; Howard and Ruder , 2018 ) . These include sentence-level tasks such as natural language inference ( Bowman et al. , 2015 ; Williams et al. , 2018 ) and paraphrasing ( Dolan and Brockett , 2005 ) , which aim to predict the relationships between sentences by analyzing them holistically , as well as token-level tasks such as named entity recognition and question answering , where models are required to produce fine-grained output at the token level ( Tjong Kim Sang and De Meulder , 2003 ; Rajpurkar et al. , 2016 ) . There are two existing strategies for applying pre-trained language representations to downstream tasks : feature-based and fine-tuning . The feature-based approach , such as ELMo ( Peters et al. , 2018a ) , uses task-specific architectures that include the pre-trained representations as additional features . The fine-tuning approach , such as the Generative Pre-trained Transformer ( OpenAI GPT ) ( Radford et al. , 2018 ) , introduces minimal task-specific parameters , and is trained on the downstream tasks by simply fine-tuning all pretrained parameters . The two approaches share the same objective function during pre-training , where they use unidirectional language models to learn general language representations . We argue that current techniques restrict the power of the pre-trained representations , especially for the fine-tuning approaches . The major limitation is that standard language models are unidirectional , and this limits the choice of architectures that can be used during pre-training . For example , in OpenAI GPT , the authors use a left-toright architecture , where every token can only attend to previous tokens in the self-attention layers of the Transformer ( Vaswani et al. , 2017 ) . Such restrictions are sub-optimal for sentence-level tasks , and could be very harmful when applying finetuning based approaches to token-level tasks such as question answering , where it is crucial to incorporate context from both directions . In this paper , we improve the fine-tuning based approaches by proposing BERT : Bidirectional Encoder Representations from Transformers . BERT alleviates the previously mentioned unidirectionality constraint by using a `` masked language model '' ( MLM ) pre-training objective , inspired by the Cloze task ( Taylor , 1953 ) . The masked language model randomly masks some of the tokens from the input , and the objective is to predict the original vocabulary id of the masked arXiv:1810.04805v2 [ cs.CL ] 24 May 2019 word based only on its context . Unlike left-toright language model pre-training , the MLM objective enables the representation to fuse the left and the right context , which allows us to pretrain a deep bidirectional Transformer . In addition to the masked language model , we also use a `` next sentence prediction '' task that jointly pretrains text-pair representations . The contributions of our paper are as follows : • We demonstrate the importance of bidirectional pre-training for language representations . Unlike Radford et al . ( 2018 ) , which uses unidirectional language models for pre-training , BERT uses masked language models to enable pretrained deep bidirectional representations . This is also in contrast to Peters et al . ( 2018a ) , which uses a shallow concatenation of independently trained left-to-right and right-to-left LMs . • We show that pre-trained representations reduce the need for many heavily-engineered taskspecific architectures . BERT is the first finetuning based representation model that achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks , outperforming many task-specific architectures . • BERT advances the state of the art for eleven NLP tasks . The code and pre-trained models are available at https : //github.com/ google-research/bert . "